Lessons Learned From the U.S. Nuclear Power Plant On-Line Monitoring Programs

Size: px
Start display at page:

Download "Lessons Learned From the U.S. Nuclear Power Plant On-Line Monitoring Programs"

Transcription

1 Lessons Learned From the U.S. Nuclear Power Plant On-Line Monitoring Programs J. Wesley Hines Nuclear Engineering Department, The University of Tennessee, Knoxville, Tennessee Eddie Davis Edan Engineering Corporation, 900 Washington St., Suite 830, Vancouver, Washington Abstract: The investigation and application of on-line monitoring programs has been ongoing for over two decades by the U.S. nuclear industry and researchers. To this date, only limited pilot installations have been demonstrated and the original objectives have changed significantly. Much of the early work centered on safety critical sensor calibration monitoring and reduction. The current focus is on both sensor and equipment monitoring. This paper presents the major lessons learned that contributed to the lengthy development process including model development and implementation issues, and the results of a recently completed cost benefit analysis. 1. Introduction and Background For the past two decades, Nuclear Power Plants (NPPs) have attempted to move towards condition-based maintenance philosophies using new technologies developed to ascertain the condition of plant equipment. Specifically, techniques have been developed to monitor the condition of sensors and their associated instrument chains. Historically, periodic manual calibrations have been used to assure sensors are operating correctly. This technique is not optimal in that sensor conditions are only checked periodically; therefore, faulty sensors can continue to operate for periods up to the calibration frequency. Faulty sensors can cause poor economic performance and unsafe conditions. Periodic techniques also cause the unnecessary calibration of instruments that are not faulted which can result in damaged equipment, plant downtime, and improper calibration under non-service conditions. Early pioneers in the use of advanced information processing techniques for instrument condition monitoring included researchers at the University of Tennessee (UT) and Argonne National Laboratory. Dr. Belle Upadhyaya was one of the original investigators in the early 1980's [Upadhyaya 1985, 1989, 1992], through a Department of Energy funded research project to investigate the application of artificial intelligence techniques to nuclear power plants. Researchers at Argonne National Laboratory continued with similar research from the late 1980's [Mott 1987] in which they developed the Multivariate State Estimation System (MSET) which has gained wide interest in the US Nuclear Industry. Lisle, IL. based SmartSignal Corporation licensed the MSET technology for applications in all industries, and subsequently extended and modified the basic MSET technology in developing their commercial Equipment Condition Monitoring (SmartSignal ecm ) software [Wegerich 2001]. The Electric Power Research Institute (EPRI) has used a product from Expert Microsystems called SureSense [Bickford 2003], which also uses the MSET algorithm. Several other US companies such as Pavillion Technologies, ASPEN IQ, and Performance Consulting Services [Griebenow 1995] have also developed sensor validation products. The major European participant in this area is the Halden Research Project where Dr. Paolo Fantoni and his multi-national research team have developed a system termed Plant Evaluation and Analysis by Neural Operators (PEANO) [Fantoni 1998, 1999] and applied it to the

2 monitoring of nuclear power plant sensors. Several other researchers have been involved with inferential sensing and on-line sensor monitoring. A survey of the methods is given by Hines [2000a]. Early EPRI research included the development of the Instrument Calibration and Monitoring Program (ICMP) for monitoring physically redundant sensors [EPRI 1993a, 1993b]. Subsequent work expanded to monitoring both redundant and non-redundant sensors. Research and development in the 1990s resulted in Topical Report TR , On-Line Monitoring of Instrument Channel Performance, developed by the EPRI/Utility On-Line Monitoring Working Group. In July 2000, the U.S. Office of Nuclear Reactor Regulation Application issued a safety evaluation (SE), which was released in September This report focused on the generic application of on-line monitoring techniques to be used as a tool for assessing instrument performance. It proposed to relax the frequency of instrument calibrations required by the U.S. nuclear power plant Technical Specifications (TS) from once every fuel cycle to once in a maximum of 8 years based on the on-line monitoring results. 1.1 EPRI On-LINE Monitoring (OLM) Group The EPRI Instrument Monitoring and Calibration (IMC) Users Group formed in 2000 with an objective to demonstrate OLM technology in operating nuclear power plants for a variety of systems and applications. A second objective is to verify that OLM is capable of identifying instrument drift or failure. The On-Line Monitoring Implementation Users Group formed in mid 2001 to demonstrate OLM in multiple applications at many nuclear power plants and has a four-year time frame. Current United States nuclear plant participants include Limerick, Salem, Sequoyah, TMI, and VC Summer using a system produced by Expert Microsystems Inc. (expmicrosys.com), and Harris and Palo Verde, which use a system developed by SmartSignal Inc. (smartsignal.com). Each of these plants is currently using OLM technology to monitor the calibration of process instrumentation. In addition to monitoring implementation, the systems have an inherent dual purpose of monitoring the condition of equipment, which is expected to improve plant performance and reliability. The Sizewell B nuclear power plant in Great Britain is using the OLM services supplied by AMS ( 1.2 Lessons Learned This paper presents a brief description of the development activities and the major lessons learned. These lessons will be divided into three main categories. First, the technology changes will be briefly discussed. Next, implementation issues will be presented along with several examples. Lastly, a recently completed cost benefit study will be summarized to show where economics will drive the future application of On-Line Monitoring technologies. 2. On Line Monitoring Techniques The OLM systems use historical plant data to develop empirical models that capture the relationships between correlated plant variables. These models are then used to verify that the relationships have not changed. A change can occur due to sensor drift, equipment faults, or operational error. The systems currently in use in the US are based on the Multivariate State Estimation Technique developed at Argonne National Laboratory (ANL) [Singer 1996, Gross 1997] and further studied at the University of Tennessee (UT) [Gribok 2000]. Numerous data-based technologies have been used by major researchers in the field including autoassociative neural networks [Fantoni 1998, Hines 1998, Upadhyaya 1992], fuzzy logic [Hines 1997], non-linear partial least squares [Qin 1992, Rasmussen 2000a], and kernel based techniques such as MSET [Singer 1996] and the Advanced Calibration Monitor (ACM) [Hansen 1994]. Three technologies have emerged and have been used in the Electric Power Industry that use different databased prediction methods: a kernel based method (MSET), a neural network based method (PEANO and the University of Tennessee AANN)), and a transformation method (NLPLS). These methods are described and compared in Hines [2000a].

3 The major lesson learned in applying empirical modeling strategies are that the methods should produce accurate results, produce repeatable and robust results, have an analytical method to estimate the uncertainty of the predictions, be easily trained and easily retrained for new or expanded operating conditions. 2.1 Accurate Results Early applications of autoassociative techniques, such as MSET, were publicized to perform well with virtually no engineering judgment necessary. One item of interest is the choice of inputs for a model. Early application limits were said to be around 100 inputs per model [EPRI 2000] with no need to choose and subgroup correlated variables. However, experience has shown that models should be constructed with groups of highly correlated sensors resulting in models commonly containing less than 30 signals [EPRI 2002a]. It has been shown that adding irrelevant signals to a model increases the prediction variance while not including a relevant variable biases the estimate [Rasmussen 2003b]. Additionally, automated techniques for sensor groupings have been developed for the MSET model [Hines 2004]. 2.2 Repeatable and Robust Results When empirical modeling techniques are applied to data sets that consist of collinear (highly correlated) data sets, ill-conditioning can result in highly accurate performance on the training data, but highly variable, inaccurate results on unseen data. Robust models perform well on data that have incorrect inputs as expected noisy environments or when a sensor input is faulted. Regularization techniques can be applied to make the predictions repeatable, robust, and with lower variability [Hines 1999, 2000b, Gribok 2000, 2001]. A summary of the methods is given in Gribok [2002], and regularization methods have been applied to many of the systems currently in use. 2.3 Uncertainty Analysis The most basic requirements outlined in the NRC safety evaluation [2000] are that of an analysis of the uncertainty in the empirical estimates. Argonne National Laboratory has performed Monte Carlo based simulations to estimate the uncertainty of MSET based technique estimations [Zavaljevski 2000, 2003]. These techniques produce average results for a particular model trained with a particular data set. Researchers at The University of Tennessee have developed analytical techniques to estimate prediction intervals for all of the major techniques (MSET, AANN, PEANO, and NLPLS). The analytical results were verified using Monte Carlo based simulations and provide the desired 95% coverage [Rasmussen 2003a, 2003b, 2004, Gribok 2004]. Each of the techniques performs well, some better than the others, on various data sets. 2.4 Ease of Training and Retraining As will be shown in section 3, it is virtually impossible for the original training data to cover the entire range of operation. The operating conditions may change over time and the models may need to be retrained to incorporate the new data. MSET based methods are not trained, but are non-parametric modeling techniques. These techniques work well in that new data vectors can simply be added to the prototype data matrix. Artificial Neural Networks require fairly long training times. Other parametric techniques, such as Non- Linear Partial Least Squares, can be trained much faster. Recently, PEANO system has incorporated a NLPLS algorithm with performed with equaled accuracy to the original AANN algorithm and can be trained in minutes versus days [Fantoni 2002].

4 3. OLM Plant Implementation In 2000, EPRI's focus moved from OLM product development to its implementation. In 2001, the On- Line Monitoring Implementation project started with a strategic role to facilitate OLM's implementation and cost effective use in numerous applications at power plants. Specifically, EPRI sponsored on-line monitoring implementations at multiple nuclear power plants. After three years of implementation and installation experience, several lessons have been learned. The major areas include data acquisition and quality, and model development, and results interpretation. 3.1 Data Acquisition and Quality In order to build a robust model for OLM, one must first collect data covering all the operating conditions in which the system is expected to operate and for which signal validation is desired. This data is historical data that has been collected and stored and may not represent the plant state due to several anomalies that commonly occur. These include interpolation errors, random data errors, missing data, loss of significant figures, stuck data, and others. Data should always be visually observed and corrected or deleted before use Interpolation Errors The first problem usually encountered in using historical data for model training is that it is usually not actual data, but instead, data resulting from compression routines normally implemented in data archival programs. For example, the PI Data Historian from OSI Software creates a data archive that is a timeseries database. However, all of the data is not stored at each collection time. Only data values that have changed by more than a tolerance are stored along with their time stamp. This method requires much less storage but results in a loss of data fidelity. When data is extracted from the historian, data values between logged data points are calculated through a simple linear interpolation. The resulting data appears to be a saw tooth time series and the correlations between sensors may be severely changed. Figure 1 below is a plot of data output by a data historian. The plot shows a perfectly linear increase in power between April 6 and April 7, although this was not the actual operation. Data collected for model training should be actual data and tolerances should be set as small as possible or not used Power (percent) Apr 6-Apr 7-Apr 8-Apr 9-Apr 10-Apr 11-Apr Time Figure 1. Data Interpolation Data Quality Issues Several data quality issues are common. These cases include Lost or missing data. Single or multiple outliers in one sensor or several.

5 Stuck data in which the data value does not update. Random data values. Unreasonable data values. Loss of significant digits. The figures below show several of these issues: Figure 2. Stuck Data Figure 3. Loss of Significant Digits

6 Figure 4. Unreasonable Data Most of these data problems can be visually identified or can be detected by a data clean up utility. These utilities remove bad data or replace it with the most probably data value using some algorithm. It is most common to delete all bad data observations from the training data set. Most OLM software systems include automated tools for data cleanup; these tools easily identify extreme outlying data but are typically insensitive to data errors that occur within the expected region of operation. The addition of bad data points in a training set can invalidate a model. The figure below shows the prediction results with (Figure 5a) and without (Figure 5b) two bad data points. The actual data is in red while the predicted data is in blue. Figure 5a. Predictions with bad data. Figure 5b. Predictions with bad data removed. 3.2 Model Development Model development is not just a simple click and go as once claimed. There are several decisions that need to be made including: Defining models and selecting relevant inputs. Selecting relevant operating regions. Selecting relevant training data.

7 Grouping the sensors into related (correlated) groups has been discussed in section 2.1. This can be done with automated systems or can be done with engineering judgment. A combination of the techniques probably works best. Most nuclear plants tend to operate for extended periods at 100 percent power and some system data tends to exhibit little variation which complicates any correlation analysis, especially for noisy signals. The model must be trained with data covering all operating regions in which it is expected to operate. These operating regions can vary significantly between nuclear plants since regions are defined by system structure, sensor values, operating procedures. One example of a system structure change is the periodic usage of standby pumps or the cycled usage of redundant pumps. A model must be trained for each operating condition for the system to work properly, but excessive training on unusual conditions may degrade the performance on the most usual operating conditions. Therefore, some plant line-ups may not ever be included in the training set. Operating conditions can also change due to equipment repair. In this case the model must be retrained to account for the new condition. In the figure below a pump impeller was repaired resulting in an increased flow rate. The sensors are operating properly before and after the repair, but they are obviously sensing different operating states. In this case, the model must be completely retrained Before Repair Flow 84 (percent) After Repair 72 Mar May Jul Sep Nov Date 1F7594A 1F7597A 1F7600A Figure 6. Repaired pump impeller. Operating conditions also change due to cyclic changes such as seasonal variations. If a model is trained during mild summers and then monitoring occurs in a hotter summer with higher cooling water temperatures, the model will not perform correctly. In this case, data from the more severe operating conditions must be added to the training data. The figure below shows an example of this anomaly.

8 Figure 7. Cyclic operating condition requiring retraining Out of the ordinary transients can also cause modeling problems. One example of this is programmed control rod changes that occur in boiling water reactors. This is a common procedure but one in which retraining might or might not be conducted, depending on the user s preference regarding false alarms. The figure below shows this example of short-term transients. Maximum values in training data Transients exceed training data limits Figure 8. Short-term transients 3.3 Results Interpretation Once a model is trained and put into operation, the predictions must be evaluated to determine if the system is operating correctly, if a sensor is drifting, if an operating condition has changed, or if an equipment failure has occurred. The choice of which has occurred can be made using logic and this logic has been programmed into expert system type advisors with some success [Wegerich 2001]. The logical rules operate on the residuals, which are the difference between the predictions and the observations. Under normal conditions, the residuals should be small random values. If only one residual grows, the hypothesis is that a sensor has degraded or failed. An example of a drifting sensor is shown below with the first plot showing the sensor value and predicted value and the second plot showing the residual.

9 Figure 9. Sensor drifting and its associated residual. If several residuals significantly differ from zero, the operating state has probably changed or an equipment failure has occurred. More in depth knowledge and engineering judgment must be used to ascertain which has occurred and a fault detection and identification system may be necessary to make this decision. Early architectures used a statistical technique termed Sequential Probability Ratio Test (SPRT), developed by Wald [1945] and improved by Gross [1992], to determine when a sensor's residual has deviated from zero. This method assumes normally distributed noise, which rarely is the case and degrades its operation. Simpler threshold checking techniques have been used with success. The thresholds have been set using different methods such as three sigma bands with sigma equal to the standard deviation of the residual from the training set. More complex techniques use changing threshold bands that switch when models change [Fantoni 1998] or change as the uncertainty in the prediction changes [Rasmussen 2003b]. 4. Cost Benefit Analysis Recently EPRI has completed a Cost Benefit Analysis Guide [EPRI, 2003]. The objective of this document was to determine the economic impact of the installation, operation, and upkeep of an On-Line Monitoring system and quantify the associated costs and benefits. This section summarizes the results presented in that document. 4.1 Costs The costs of an on-line implementation include software licensing, equipment, model development, training, and maintenance. If the system is used to monitor Technical Specification sensors to defer manual calibrations, then additional costs will be incurred to obtain a license amendment. These costs are summarized in Table 1 below. The expected costs are very sensitive to the software costs and the values below apply specifically to the Expert Microsystems SureSense software used by the EPRI OLM Implementation project.

10 Table 1. Costs of On Line Monitoring Implementation [EPRI 2003] 4.2 Benefits The benefits of an on-line monitoring system include direct benefits from a reduction in manual calibrations, and indirect benefits including performance enhancements and equipment monitoring. It has been determined that the cost of a manual calibration is approximately $910 for one sensor. The number of safety critical sensors covered by Technical Specifications commonly ranges between 60 and 100 sensors. However, more than 200 sensors are suitable for calibration monitoring and the range of savings depends on the number of calibrations avoided each cycle, which depend on the number of sensor being monitored. The typical anticipated savings are 50 calibrations deferred each operating cycle $45, calibrations deferred each operating cycle $68, calibrations deferred each operating cycle $91,000 Figure 1 below graphically shows the payback due to the installation of an OLM installation considering the benefits of calibration reductions. The payback period is strongly affected by the number of sensors being monitored. A system monitoring 300 sensors has a payback of 6 years while a system monitoring only 100 sensors may never have a positive net present value. This shows that the techniques must be applied to non-technical Specification sensors for the investment to be worthy of consideration. It is also apparent that the benefits of calibration reduction alone may not be a strong incentive for installation of an OLM system and other indirect benefits should be considered.

11 Figure 1. Payback Analysis of OLM Installation The indirect benefits related to OLM are more difficult to quantify. One example is that of being able to schedule maintenance for failed sensors. In 2001 a participating NPP detected a first stage turbine pressure sensor drift. This sensor is an input to a pressure control system and has a redundancy of only two. Therefore without an OLM system, the operator would not have been able to justify which of the two sensors was drifting and would have had to immediately perform maintenance. Because the faulty sensor was readily identified, the plant was able to continue normal operation and maintenance was scheduled for a more opportune time. Additionally, the time to troubleshoot sensor anomalies is reduced with OLM. Indirect benefits can also be attributed to increased performance. Several plants use performance monitoring software to increase thermal efficiency. If a faulty sensor were used as input to the performance monitoring system, incorrect plant operational changes could be made that would reduce the performance of the plant. Having validated signals as inputs to these systems can have an economic advantage. The benefits of on-line equipment monitoring are also difficult to quantify but may be extremely important. The benefits range from more efficient maintenance scheduling to a reduction in down-time. The largest potential savings comes from the possible avoidance of an incident. A study of the loss of power incidents at eight selected U.S. units between 2000 and 2004 shows that the average number of incidents is 5.5. The average dollar loss per incident ranges from 0.6 million to 6.2 million with a mean estimate of 1.5 million. These values lead to a loss of ranging from 3.4 million to 33.9 million with a mean of 8.1 million. It is apparent that just one avoided incident would pay for the installation costs many times over. However, a cursory investigation also shows that only a small percentage of the incidents would have been avoided through the use of an OLM system. 5. Conclusions The development and application of On-Line Monitoring systems has occurred over the past 20 years. Through that time period much has been learned about improving the modeling techniques, implementing the system at a plant site, evaluating the results, and the economically basis for such an installation. The original objective of extending Technical Specification sensor calibrations to meet extended fuel cycles has changed to monitoring both safety and non-safety related signals, performance, and equipment. As plants fully field these technologies, the efforts and experiences of plant personnel, researchers, and EPRI project managers will prove invaluable.

12 References Bickford, R., Holzworth, R.E., R.D. Griebenow, and A. Hussey (2003), "An Advanced Equipment Condition Monitoring System for Power Plants", Transactions of the American Nuclear Society, New Orleans, LA, Nov 16-20, EPRI (1993a), Instrument Calibration and Monitoring Program, Volume 1: Basis for the Method, EPRI, Palo Alto, CA: V1. EPRI (199b3), Instrument Calibration and Monitoring Program, Volume 2: Failure Modes and Effects Analysis, EPRI, Palo Alto, CA: V2. EPRI (2000), On-Line Monitoring of Instrument Channel Performance, EPRI, Palo Alto, CA: EPRI (2002a), Plant Systems Modeling Guidelines to Implement On-Line Monitoring, EPRI, Palo Alto, CA: EPRI (2002b), On-Line Monitoring Implementation Guidelines, EPRI, Palo Alto, CA: EPRI (2002c), Implementation of On-Line Monitoring for Technical Specification Instruments, EPRI, Palo Alto, CA: EPRI (2003), On-Line Monitoring Cost Benefit Guide, Final Report, EPRI, Palo Alto, CA: Fantoni, P., S. Figedy, A. Racz, (1998), "A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power Plant Data", FLINS-98, Antwerpen, Belgium. Fantoni, P., (1999), "On-Line Calibration Monitoring of Process Instrumentation in Power Plants", EPRI Plant Maintenance Conference, Atlanta, Georgia, June 21, Fantoni, P., M. Hoffmann, B. Rasmussen, J.W. Hines, and A. Kirschner, (2002), "The use of non linear partial least square methods for on-line process monitoring as an alternative to artificial neural networks," 5 th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS), Gent, Belgium, Sept Gribok, A.V., J.W. Hines, R.E. Uhrig (2000), "Use of Kernal Based Techniques for Sensor Validation in Nuclear Power Plants", The Third American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation and Control and Human-Machine Interface Technologies, Washington DC, November 13-17, Gribok, A.V., J.W. Hines, I Attieh, and R.E. Uhrig, (2000), "Stochastic Regularization of Feedwater Flow Rate Evaluation for Venturi the Meter Fouling Problem in Nuclear Power Plants", Inverse Problems in Engineering. Gribok, A.V., J.W. Hines, I Attieh, and R.E. Uhrig, (2001), "Regularization of Feedwater Flow Rate Evaluation for the Venturi Meter Fouling Problems in Nuclear Power Plants", Nuclear Technology,Vol. 134, No. 1, April Gribok, A.V., J.W. Hines, A. Urmanov, and R.E. Uhrig, "Regularization of Ill-Posed Surveillance and Diagnostic Measurements", Power Plant Surveillance and Diagnostics, eds. Da Ruan and P. Fantoni, Springer, Gribok, A.V., J.W. Hines, A.M. Urmanov, (2004), "Uncertainty Analysis of Memory Based Sensor Validation Techniques", accepted for publication in the special issue of Real Time Systems on "Applications of Intelligent Real-Time Systems for Nuclear Engineering". Griebenow, R.D., A.L. Sudduth (1995), "Applied Pattern Recognition for Plant Monitoring and Data Validation", The1995 ISA POWID Conference.

13 Gross, K.C. (1992), "Spectrum-Transformed Sequential Testing Method for Signal Validation Applications", 8 th Power Plant Dynamics, Control & Testing Symp., Knoxville, Tennessee, Vol. I, May, 1992, pp Gross, K.C., R.M. Singer, J.P. Herzog, R. VanAlstine and S.W. Wegerich (1997), "Application of a Model-based Fault Detection System to Nuclear Plant Signals", Proceedings, Intelligent System Applications to Power Systems, (ISAP (&), Seoul, Korea, July 6-10, pp Hansen, E.J., and M.B. Caudill (1994), Similarity Based Regression: Applied Advanced Pattern Recognition for Power Plant Analysis ; E.J. Hansen, M.B. Caudill; 1994 EPRI-ASME Heat Rate Improvement Conference; Baltimore, Maryland. Hines, J.W., and D.J. Wrest (1997), "Signal Validation Using an Adaptive Neural Fuzzy Inference System", Nuclear Technology, August, pp Hines, J.W., and R.E. Uhrig, (1998), "Use of Autoassociative Neural Networks for Signal Validation", Journal of Intelligent and Robotic Systems, Kluwer Academic Press, February, pp Hines, J.W., A.V. Gribok, I. Attieh, and R.E. Uhrig (1999), "Regularization Methods for Inferential Sensing in Nuclear Power Plants", Fuzzy Systems and Soft Computing in Nuclear Engineering, Ed. Da Ruan, Springer, Hines, J.W. and B. Rasmussen (2000a), "On-Line Sensor Calibration Verification: "A Survey"", 14th International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, Manchester, England, September, Hines, J.W., A.V. Gribok, R.E. Uhrig, and I. Attieh, (2000b), "Neural network regularization techniques for a sensor validation system," Transactions of the American Nuclear Society, San Diego, California, June 4-8. Hines, J.W., A. Usynin, and S. Wegerich (2004), "Autoassociative Model Input Variable Selection for Process Modeling", 58th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, Virginia, April 26-30, Mott, Young, and R.W. King (1987), Pattern Recognition Software for Plant Surveillance, US DOE Report. Qin, S.J., and T.J. McAvoy (1992), "Nonlinear PLS Modeling Using Neural Networks," Computers in Chemical Engineering, vol. 16, n. 4, pp Rasmussen, B., J.W. Hines, and R.E. Uhrig (2000), "Nonlinear Partial Least Squares Modeling for Instrument Surveillance and Calibration Verification, Proc. Maintenance and Reliability Conference, Knoxville, TN. Rasmussen, B. J.W. Hines, and A.V. Gribok (2003a), "An Applied Comparison of the Prediction Intervals of Common Empirical Modeling Strategies", Brandon Rasmussen, Andrei Gribok, and J. Wesley Hines, Proc. Maintenance and Reliability Conference, Knoxville, TN. Rasmussen, B., (2003b), "Prediction Interval Estimation Techniques for Empirical Modeling Strategies and their Applications to Signal Validation Tasks", Ph.D. dissertation, Nuclear Engineering Department, The University of Tennessee, Knoxville. Rasmussen, B. and J.W. Hines (2004), "Uncertainty Estimation Techniques for Empirical Model Based Condition Monitoring", 6th International FLINS Conference on Applied Computational Intelligence, Blankenberge, Belgium, Sept 1-4, Singer, R.M., K.C. Gross, J.P. Herzog, R.W. King and S.W. Wegerich (1996), "Model-Based Nuclear Power Plant Monitoring and Fault Detection: Theoretical Foundations", Proc. 9th Intl. Conf. on Intelligent Systems Applications to Power Systems, Seoul, Korea. Upadhyaya, B.R., (1985), Sensor Failure Detection and Estimation, Nuclear Safety.

14 Upadhyaya, B.R., and K. Holbert (1989), Development and Testing of an Integrated Signal Validation System for Nuclear Power Plants, DOE Contract DE-AC02-86NE Upadhyaya, B.R., and E. Eryurek (1992), "Application of Neural Networks for Sensor Validation and Plant Monitoring," Nuclear Technology, vol. 97, pp , February, Wald, A (1945), "Sequential Tests of Statistical Hypotheses," Annals of Mathematical Statistics, Vol. 16, pp Wegerich, S, R. Singer, J. Herzog, and A. Wilks (2001), "Challenges Facing Equipment Condition Monitoring Systems", Proc. Maintenance and Reliability Conference, Gatlinburg, TN. Zavaljevski, N and K. Gross (2000), "Uncertainty Analysis for Multivariate State Estimation in Safety Critical and Mission Critical Maintenance Applications", Proc. Maintenance and Reliability Conference, Knoxville, TN. Zavaljevski, N., A. Miron, C. Yu, and E. Davis (2003), "Uncertainty Analysis for the Multivariate State Estimation Technique (MSET) Based on Latin Hypercube Sampling and Wavelet De-Noising", Transaction of the American Nuclear Society, New Orleans, LA, November

DEVELOPMENT OF AN ONLINE PREDICTIVE MONITORING SYSTEM FOR POWER GENERATING PLANTS

DEVELOPMENT OF AN ONLINE PREDICTIVE MONITORING SYSTEM FOR POWER GENERATING PLANTS DEVELOPMENT OF AN ONLINE PREDICTIVE MONITORING SYSTEM FOR POWER GENERATING PLANTS Randall Bickford Expert Microsystems, Inc. Orangevale, California rando@expmicrosys.com Richard Rusaw South Carolina Electric

More information

Technical Review of On-Line Monitoring Techniques for Performance Assessment

Technical Review of On-Line Monitoring Techniques for Performance Assessment NUREG/CR-6895 Technical Review of On-Line Monitoring Techniques for Performance Assessment Volume 1: State-of-the-Art University of Tennessee U.S. Nuclear Regulatory Commission Office of Nuclear Regulatory

More information

SureSense Software Suite Overview

SureSense Software Suite Overview SureSense Software Overview Eliminate Failures, Increase Reliability and Safety, Reduce Costs and Predict Remaining Useful Life for Critical Assets Using SureSense and Health Monitoring Software What SureSense

More information

INTEGRATION OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR NUCLEAR POWER PLANT SURVEILLANCE AND DIAGNOSTICS DESCRIPTION OF PROJECT

INTEGRATION OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR NUCLEAR POWER PLANT SURVEILLANCE AND DIAGNOSTICS DESCRIPTION OF PROJECT INTEGRATION OF ARTIFICIAL INTELLIGENCE SYSTEMS FOR NUCLEAR POWER PLANT SURVEILLANCE AND DIAGNOSTICS Robert E. Uhrig and J. Wesley Hines Department of Nuclear Engineering, University of Tennessee, Knoxville,

More information

Predictive Analytics uses data

Predictive Analytics uses data Predictive Analytics in Equipment Reliability Programs Predictive Analytics in conjunction with Threat Based Maintenance (TBM ) reduces and, in some cases, can eliminate the need to perform time-based

More information

Overview of UT Nuclear Engineering Department and Faculty Research Interests

Overview of UT Nuclear Engineering Department and Faculty Research Interests Overview of UT Department and Faculty Research Interests Presented by H. L. Dodds IBM Professor of Engineering and Department Head UT Department To ORNL Summer Interns July 13, 2005 Presentation Overview

More information

Proactive Asset Management with IIoT and Analytics

Proactive Asset Management with IIoT and Analytics Proactive Asset Management with IIoT and Analytics by Ralph Rio in Industrial Internet of Things, Analytics & Big Data Summary The Industrial Internet of Things (IIoT) with advanced analytics, offers new

More information

Gerard Mc Nulty Systems Optimisation Ltd gmcnulty@iol.ie/0876697867 BA.,B.A.I.,C.Eng.,F.I.E.I

Gerard Mc Nulty Systems Optimisation Ltd gmcnulty@iol.ie/0876697867 BA.,B.A.I.,C.Eng.,F.I.E.I Gerard Mc Nulty Systems Optimisation Ltd gmcnulty@iol.ie/0876697867 BA.,B.A.I.,C.Eng.,F.I.E.I Data is Important because it: Helps in Corporate Aims Basis of Business Decisions Engineering Decisions Energy

More information

SPPA-D3000 Plant Monitor Technical Description

SPPA-D3000 Plant Monitor Technical Description SPPA-D3000 Plant Monitor Technical Description Model-based monitoring and early fault detection for components and processes May 2008 Answers for energy. 1 Siemens AG 2008. All Rights Reserved Contents

More information

EDDYONE AUTOMATED ANALYSIS OF PWR/WWER STEAM GENERATOR TUBES EDDY CURRENT DATA

EDDYONE AUTOMATED ANALYSIS OF PWR/WWER STEAM GENERATOR TUBES EDDY CURRENT DATA EDDYONE AUTOMATED ANALYSIS OF PWR/WWER STEAM GENERATOR TUBES EDDY CURRENT DATA ABSTRACT Dr.sc. Berislav Nadinic dipl.ing. R&D Department Manager INETEC Institute for Nuclear Technology Dolenica 28, 0000

More information

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics. Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

More information

Another Look at Sensitivity of Bayesian Networks to Imprecise Probabilities

Another Look at Sensitivity of Bayesian Networks to Imprecise Probabilities Another Look at Sensitivity of Bayesian Networks to Imprecise Probabilities Oscar Kipersztok Mathematics and Computing Technology Phantom Works, The Boeing Company P.O.Box 3707, MC: 7L-44 Seattle, WA 98124

More information

System Aware Cyber Security

System Aware Cyber Security System Aware Cyber Security Application of Dynamic System Models and State Estimation Technology to the Cyber Security of Physical Systems Barry M. Horowitz, Kate Pierce University of Virginia April, 2012

More information

Equipment Performance Monitoring

Equipment Performance Monitoring Equipment Performance Monitoring Web-based equipment monitoring cuts costs and increases equipment uptime This document explains the process of how AMS Performance Monitor operates to enable organizations

More information

RAVEN: A GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework

RAVEN: A GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework INL/CON-13-28360 PREPRINT RAVEN: A GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework ANS Annual Meeting C. Rabiti D. Mandelli A. Alfonsi J. J. Cogliati R. Kinoshita D. Gaston R. Martineau

More information

Web-Based Economic Optimization Tools for Reducing Operating Costs

Web-Based Economic Optimization Tools for Reducing Operating Costs Web-Based Economic Tools for Reducing Operating Costs Authors: Keywords: Abstract: Jeffery Williams Power & Water Solutions, Inc. David Egelston Power & Water Solutions, Inc. Browsers, Economics, Linear

More information

A Data Analytic Engine Towards Self-Management of Cyber-Physical Systems

A Data Analytic Engine Towards Self-Management of Cyber-Physical Systems 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops A Data Analytic Engine Towards Self-Management of Cyber-Physical Systems Min Ding, Haifeng Chen, Abhishek Sharma, Kenji

More information

APPENDIX N. Data Validation Using Data Descriptors

APPENDIX N. Data Validation Using Data Descriptors APPENDIX N Data Validation Using Data Descriptors Data validation is often defined by six data descriptors: 1) reports to decision maker 2) documentation 3) data sources 4) analytical method and detection

More information

A HYBRID RULE BASED FUZZY-NEURAL EXPERT SYSTEM FOR PASSIVE NETWORK MONITORING

A HYBRID RULE BASED FUZZY-NEURAL EXPERT SYSTEM FOR PASSIVE NETWORK MONITORING A HYBRID RULE BASED FUZZY-NEURAL EXPERT SYSTEM FOR PASSIVE NETWORK MONITORING AZRUDDIN AHMAD, GOBITHASAN RUDRUSAMY, RAHMAT BUDIARTO, AZMAN SAMSUDIN, SURESRAWAN RAMADASS. Network Research Group School of

More information

SOFTWARE PERFORMANCE EVALUATION ALGORITHM EXPERIMENT FOR IN-HOUSE SOFTWARE USING INTER-FAILURE DATA

SOFTWARE PERFORMANCE EVALUATION ALGORITHM EXPERIMENT FOR IN-HOUSE SOFTWARE USING INTER-FAILURE DATA I.J.E.M.S., VOL.3(2) 2012: 99-104 ISSN 2229-6425 SOFTWARE PERFORMANCE EVALUATION ALGORITHM EXPERIMENT FOR IN-HOUSE SOFTWARE USING INTER-FAILURE DATA *Jimoh, R. G. & Abikoye, O. C. Computer Science Department,

More information

A Regime-Switching Model for Electricity Spot Prices. Gero Schindlmayr EnBW Trading GmbH g.schindlmayr@enbw.com

A Regime-Switching Model for Electricity Spot Prices. Gero Schindlmayr EnBW Trading GmbH g.schindlmayr@enbw.com A Regime-Switching Model for Electricity Spot Prices Gero Schindlmayr EnBW Trading GmbH g.schindlmayr@enbw.com May 31, 25 A Regime-Switching Model for Electricity Spot Prices Abstract Electricity markets

More information

Model-Based Performance Monitoring: Review of Diagnostic Methods and Chiller Case Study

Model-Based Performance Monitoring: Review of Diagnostic Methods and Chiller Case Study LBNL-45949 CD-425 ACEEE 2000 Summer Study on Energy Efficiency in Buildings, Efficiency and Sustainability, August 20-25, 2000, Asilomar Conference Center, Pacific Grove, CA, and published in the Proceedings.

More information

Bootstrapping Big Data

Bootstrapping Big Data Bootstrapping Big Data Ariel Kleiner Ameet Talwalkar Purnamrita Sarkar Michael I. Jordan Computer Science Division University of California, Berkeley {akleiner, ameet, psarkar, jordan}@eecs.berkeley.edu

More information

Wireless Sensor Network Performance Monitoring

Wireless Sensor Network Performance Monitoring Wireless Sensor Network Performance Monitoring Yaqoob J. Al-raisi & David J. Parish High Speed Networks Group Loughborough University MSN Coseners 12-13th 13th July 2007 Overview The problem we are trying

More information

Application and results of automatic validation of sewer monitoring data

Application and results of automatic validation of sewer monitoring data Application and results of automatic validation of sewer monitoring data M. van Bijnen 1,3 * and H. Korving 2,3 1 Gemeente Utrecht, P.O. Box 8375, 3503 RJ, Utrecht, The Netherlands 2 Witteveen+Bos Consulting

More information

The Advantages of Enterprise Historians vs. Relational Databases

The Advantages of Enterprise Historians vs. Relational Databases GE Intelligent Platforms The Advantages of Enterprise Historians vs. Relational Databases Comparing Two Approaches for Data Collection and Optimized Process Operations The Advantages of Enterprise Historians

More information

Beamex. Calibration White Paper. www.beamex.com info@beamex.com. How often should instruments be calibrated

Beamex. Calibration White Paper. www.beamex.com info@beamex.com. How often should instruments be calibrated Beamex Calibration White Paper info@beamex.com How often should instruments be calibrated How often should instruments be calibrated Plants can improve their efficiency and reduce costs by performing calibration

More information

TAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW. Resit Unal. Edwin B. Dean

TAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW. Resit Unal. Edwin B. Dean TAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW Resit Unal Edwin B. Dean INTRODUCTION Calibrations to existing cost of doing business in space indicate that to establish human

More information

SOFTWARE-IMPLEMENTED SAFETY LOGIC Angela E. Summers, Ph.D., P.E., President, SIS-TECH Solutions, LP

SOFTWARE-IMPLEMENTED SAFETY LOGIC Angela E. Summers, Ph.D., P.E., President, SIS-TECH Solutions, LP SOFTWARE-IMPLEMENTED SAFETY LOGIC Angela E. Summers, Ph.D., P.E., President, SIS-TECH Solutions, LP Software-Implemented Safety Logic, Loss Prevention Symposium, American Institute of Chemical Engineers,

More information

Selecting Sensors for Safety Instrumented Systems per IEC 61511 (ISA 84.00.01 2004)

Selecting Sensors for Safety Instrumented Systems per IEC 61511 (ISA 84.00.01 2004) Selecting Sensors for Safety Instrumented Systems per IEC 61511 (ISA 84.00.01 2004) Dale Perry Worldwide Pressure Marketing Manager Emerson Process Management Rosemount Division Chanhassen, MN 55317 USA

More information

M & V Guidelines for HUD Energy Performance Contracts Guidance for ESCo-Developed Projects 1/21/2011

M & V Guidelines for HUD Energy Performance Contracts Guidance for ESCo-Developed Projects 1/21/2011 M & V Guidelines for HUD Energy Performance Contracts Guidance for ESCo-Developed Projects 1/21/2011 1) Purpose of the HUD M&V Guide This document contains the procedures and guidelines for quantifying

More information

White Paper. Making Sense of the Data-Oriented Tools Available to Facility Managers. Find What Matters. Version 1.1 Oct 2013

White Paper. Making Sense of the Data-Oriented Tools Available to Facility Managers. Find What Matters. Version 1.1 Oct 2013 White Paper Making Sense of the Data-Oriented Tools Available to Facility Managers Version 1.1 Oct 2013 Find What Matters Making Sense the Data-Oriented Tools Available to Facility Managers INTRODUCTION

More information

An Introduction to. Metrics. used during. Software Development

An Introduction to. Metrics. used during. Software Development An Introduction to Metrics used during Software Development Life Cycle www.softwaretestinggenius.com Page 1 of 10 Define the Metric Objectives You can t control what you can t measure. This is a quote

More information

APPLICATION OF DATA MINING TECHNIQUES FOR BUILDING SIMULATION PERFORMANCE PREDICTION ANALYSIS. email paul@esru.strath.ac.uk

APPLICATION OF DATA MINING TECHNIQUES FOR BUILDING SIMULATION PERFORMANCE PREDICTION ANALYSIS. email paul@esru.strath.ac.uk Eighth International IBPSA Conference Eindhoven, Netherlands August -4, 2003 APPLICATION OF DATA MINING TECHNIQUES FOR BUILDING SIMULATION PERFORMANCE PREDICTION Christoph Morbitzer, Paul Strachan 2 and

More information

Social Innovation through Utilization of Big Data

Social Innovation through Utilization of Big Data Social Innovation through Utilization of Big Data Hitachi Review Vol. 62 (2013), No. 7 384 Shuntaro Hitomi Keiro Muro OVERVIEW: The analysis and utilization of large amounts of actual operational data

More information

Beamex. Calibration White Paper. www.beamex.com info@beamex.com. How often should instruments be calibrated?

Beamex. Calibration White Paper. www.beamex.com info@beamex.com. How often should instruments be calibrated? Beamex Calibration White Paper info@beamex.com How often should instruments be calibrated? How often should instruments be calibrated? An analysis will tell. Plants can improve their efficiency and reduce

More information

Chapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network

Chapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network Chapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network Qian Wu, Yahui Wang, Long Zhang and Li Shen Abstract Building electrical system fault diagnosis is the

More information

Graduate Certificate in Systems Engineering

Graduate Certificate in Systems Engineering Graduate Certificate in Systems Engineering Systems Engineering is a multi-disciplinary field that aims at integrating the engineering and management functions in the development and creation of a product,

More information

Soft-Computing Models for Building Applications - A Feasibility Study (EPSRC Ref: GR/L84513)

Soft-Computing Models for Building Applications - A Feasibility Study (EPSRC Ref: GR/L84513) Soft-Computing Models for Building Applications - A Feasibility Study (EPSRC Ref: GR/L84513) G S Virk, D Azzi, K I Alkadhimi and B P Haynes Department of Electrical and Electronic Engineering, University

More information

DEXTER. Ship Health Monitoring Software. Efficiency & Reliability. Smart Solutions for

DEXTER. Ship Health Monitoring Software. Efficiency & Reliability. Smart Solutions for DEXTER TM Ship Health Monitoring Software Smart Solutions for Energy Efficiency & Reliability DEXTER Leveraging Data for Cost Avoidance In today s economic and environmental climate, efficiency is critical.

More information

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVI - Fault Accomodation Using Model Predictive Methods - Jovan D. Bošković and Raman K.

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVI - Fault Accomodation Using Model Predictive Methods - Jovan D. Bošković and Raman K. FAULT ACCOMMODATION USING MODEL PREDICTIVE METHODS Scientific Systems Company, Inc., Woburn, Massachusetts, USA. Keywords: Fault accommodation, Model Predictive Control (MPC), Failure Detection, Identification

More information

An innovative approach combining industrial process data analytics and operator participation to implement lean energy programs: A Case Study

An innovative approach combining industrial process data analytics and operator participation to implement lean energy programs: A Case Study An innovative approach combining industrial process data analytics and operator participation to implement lean energy programs: A Case Study Philippe Mack, Pepite SA Joanna Huddleston, Pepite SA Bernard

More information

Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network

Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network Dušan Marček 1 Abstract Most models for the time series of stock prices have centered on autoregressive (AR)

More information

EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS

EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS Author Marie Schnitzer Director of Solar Services Published for AWS Truewind October 2009 Republished for AWS Truepower: AWS Truepower, LLC

More information

ALARM PERFORMANCE IMPROVEMENT DURING ABNORMAL SITUATIONS

ALARM PERFORMANCE IMPROVEMENT DURING ABNORMAL SITUATIONS ALARM PERFORMANCE IMPROVEMENT DURING ABNORMAL SITUATIONS Peter Andow Honeywell Hi-Spec Solutions, Southampton, UK The process industries are continually facing new challenges to increase throughput, improve

More information

Introduction. Chapter 1

Introduction. Chapter 1 Chapter 1 Introduction The area of fault detection and diagnosis is one of the most important aspects in process engineering. This area has received considerable attention from industry and academia because

More information

Data Cleansing for Remote Battery System Monitoring

Data Cleansing for Remote Battery System Monitoring Data Cleansing for Remote Battery System Monitoring Gregory W. Ratcliff Randall Wald Taghi M. Khoshgoftaar Director, Life Cycle Management Senior Research Associate Director, Data Mining and Emerson Network

More information

Introduction. Background

Introduction. Background Predictive Operational Analytics (POA): Customized Solutions for Improving Efficiency and Productivity for Manufacturers using a Predictive Analytics Approach Introduction Preserving assets and improving

More information

Health Management for In-Service Gas Turbine Engines

Health Management for In-Service Gas Turbine Engines Health Management for In-Service Gas Turbine Engines PHM Society Meeting San Diego, CA October 1, 2009 Thomas Mooney GE-Aviation DES-1474-1 Agenda Legacy Maintenance Implementing Health Management Choosing

More information

COMBINING THE METHODS OF FORECASTING AND DECISION-MAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES

COMBINING THE METHODS OF FORECASTING AND DECISION-MAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES COMBINING THE METHODS OF FORECASTING AND DECISION-MAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES JULIA IGOREVNA LARIONOVA 1 ANNA NIKOLAEVNA TIKHOMIROVA 2 1, 2 The National Nuclear Research

More information

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012 Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts

More information

Big Data Collection and Utilization for Operational Support of Smarter Social Infrastructure

Big Data Collection and Utilization for Operational Support of Smarter Social Infrastructure Hitachi Review Vol. 63 (2014), No. 1 18 Big Data Collection and Utilization for Operational Support of Smarter Social Infrastructure Kazuaki Iwamura Hideki Tonooka Yoshihiro Mizuno Yuichi Mashita OVERVIEW:

More information

Long Term Operation R&D to Investigate the Technical Basis for Life Extension and License Renewal Decisions

Long Term Operation R&D to Investigate the Technical Basis for Life Extension and License Renewal Decisions Long Term Operation R&D to Investigate the Technical Basis for Life Extension and License Renewal Decisions John Gaertner Technical Executive Electric Power Research Institute Charlotte, North Carolina,

More information

Maximization versus environmental compliance

Maximization versus environmental compliance Maximization versus environmental compliance Increase use of alternative fuels with no risk for quality and environment Reprint from World Cement March 2005 Dr. Eduardo Gallestey, ABB, Switzerland, discusses

More information

A Spectral Clustering Approach to Validating Sensors via Their Peers in Distributed Sensor Networks

A Spectral Clustering Approach to Validating Sensors via Their Peers in Distributed Sensor Networks A Spectral Clustering Approach to Validating Sensors via Their Peers in Distributed Sensor Networks H. T. Kung Dario Vlah {htk, dario}@eecs.harvard.edu Harvard School of Engineering and Applied Sciences

More information

Enterprise Asset Performance Management

Enterprise Asset Performance Management Application Solution Enterprise Asset Performance Management for Power Utilities Using the comprehensive Enterprise Asset Performance Management solution offered by Schneider Electric, power utilities

More information

Testing Low Power Designs with Power-Aware Test Manage Manufacturing Test Power Issues with DFTMAX and TetraMAX

Testing Low Power Designs with Power-Aware Test Manage Manufacturing Test Power Issues with DFTMAX and TetraMAX White Paper Testing Low Power Designs with Power-Aware Test Manage Manufacturing Test Power Issues with DFTMAX and TetraMAX April 2010 Cy Hay Product Manager, Synopsys Introduction The most important trend

More information

Dynamic Neural Networks for Actuator Fault Diagnosis: Application to the DAMADICS Benchmark Problem

Dynamic Neural Networks for Actuator Fault Diagnosis: Application to the DAMADICS Benchmark Problem Dynamic Neural Networks for Actuator Fault Diagnosis: Application to the DAMADICS Benchmark Problem Krzysztof PATAN and Thomas PARISINI University of Zielona Góra Poland e-mail: k.patan@issi.uz.zgora.pl

More information

NTC Project: S01-PH10 (formerly I01-P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling

NTC Project: S01-PH10 (formerly I01-P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling 1 Forecasting Women s Apparel Sales Using Mathematical Modeling Celia Frank* 1, Balaji Vemulapalli 1, Les M. Sztandera 2, Amar Raheja 3 1 School of Textiles and Materials Technology 2 Computer Information

More information

Temperature Control Loop Analyzer (TeCLA) Software

Temperature Control Loop Analyzer (TeCLA) Software Temperature Control Loop Analyzer (TeCLA) Software F. Burzagli - S. De Palo - G. Santangelo (Alenia Spazio) Fburzagl@to.alespazio.it Foreword A typical feature of an active loop thermal system is to guarantee

More information

Logging of RF Power Measurements

Logging of RF Power Measurements Logging of RF Power Measurements By Orwill Hawkins Logging of measurement data is critical for effective trend, drift and Exploring the use of RF event analysis of various processes. For RF power measurements,

More information

Quantifying Seasonal Variation in Cloud Cover with Predictive Models

Quantifying Seasonal Variation in Cloud Cover with Predictive Models Quantifying Seasonal Variation in Cloud Cover with Predictive Models Ashok N. Srivastava, Ph.D. ashok@email.arc.nasa.gov Deputy Area Lead, Discovery and Systems Health Group Leader, Intelligent Data Understanding

More information

Increase System Efficiency with Condition Monitoring. Embedded Control and Monitoring Summit National Instruments

Increase System Efficiency with Condition Monitoring. Embedded Control and Monitoring Summit National Instruments Increase System Efficiency with Condition Monitoring Embedded Control and Monitoring Summit National Instruments Motivation of Condition Monitoring Impeller Contact with casing and diffuser vanes Bent

More information

Machine Learning in Statistical Arbitrage

Machine Learning in Statistical Arbitrage Machine Learning in Statistical Arbitrage Xing Fu, Avinash Patra December 11, 2009 Abstract We apply machine learning methods to obtain an index arbitrage strategy. In particular, we employ linear regression

More information

INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr.

INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr. INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr. Meisenbach M. Hable G. Winkler P. Meier Technology, Laboratory

More information

SINGULAR SPECTRUM ANALYSIS HYBRID FORECASTING METHODS WITH APPLICATION TO AIR TRANSPORT DEMAND

SINGULAR SPECTRUM ANALYSIS HYBRID FORECASTING METHODS WITH APPLICATION TO AIR TRANSPORT DEMAND SINGULAR SPECTRUM ANALYSIS HYBRID FORECASTING METHODS WITH APPLICATION TO AIR TRANSPORT DEMAND K. Adjenughwure, Delft University of Technology, Transport Institute, Ph.D. candidate V. Balopoulos, Democritus

More information

A Study on the Comparison of Electricity Forecasting Models: Korea and China

A Study on the Comparison of Electricity Forecasting Models: Korea and China Communications for Statistical Applications and Methods 2015, Vol. 22, No. 6, 675 683 DOI: http://dx.doi.org/10.5351/csam.2015.22.6.675 Print ISSN 2287-7843 / Online ISSN 2383-4757 A Study on the Comparison

More information

Multivariate Tools for Modern Pharmaceutical Control FDA Perspective

Multivariate Tools for Modern Pharmaceutical Control FDA Perspective Multivariate Tools for Modern Pharmaceutical Control FDA Perspective IFPAC Annual Meeting 22 January 2013 Christine M. V. Moore, Ph.D. Acting Director ONDQA/CDER/FDA Outline Introduction to Multivariate

More information

OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS

OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS CLARKE, Stephen R. Swinburne University of Technology Australia One way of examining forecasting methods via assignments

More information

Nuclear Power Plant Electrical Power Supply System Requirements

Nuclear Power Plant Electrical Power Supply System Requirements 1 Nuclear Power Plant Electrical Power Supply System Requirements Željko Jurković, Krško NPP, zeljko.jurkovic@nek.si Abstract Various regulations and standards require from electrical power system of the

More information

MONITORING AND DIAGNOSIS OF A MULTI-STAGE MANUFACTURING PROCESS USING BAYESIAN NETWORKS

MONITORING AND DIAGNOSIS OF A MULTI-STAGE MANUFACTURING PROCESS USING BAYESIAN NETWORKS MONITORING AND DIAGNOSIS OF A MULTI-STAGE MANUFACTURING PROCESS USING BAYESIAN NETWORKS Eric Wolbrecht Bruce D Ambrosio Bob Paasch Oregon State University, Corvallis, OR Doug Kirby Hewlett Packard, Corvallis,

More information

Building Energy Management: Using Data as a Tool

Building Energy Management: Using Data as a Tool Building Energy Management: Using Data as a Tool Issue Brief Melissa Donnelly Program Analyst, Institute for Building Efficiency, Johnson Controls October 2012 1 http://www.energystar. gov/index.cfm?c=comm_

More information

Regression Modeling Strategies

Regression Modeling Strategies Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions

More information

Handling attrition and non-response in longitudinal data

Handling attrition and non-response in longitudinal data Longitudinal and Life Course Studies 2009 Volume 1 Issue 1 Pp 63-72 Handling attrition and non-response in longitudinal data Harvey Goldstein University of Bristol Correspondence. Professor H. Goldstein

More information

Ziegler-Nichols-Based Intelligent Fuzzy PID Controller Design for Antenna Tracking System

Ziegler-Nichols-Based Intelligent Fuzzy PID Controller Design for Antenna Tracking System Ziegler-Nichols-Based Intelligent Fuzzy PID Controller Design for Antenna Tracking System Po-Kuang Chang, Jium-Ming Lin Member, IAENG, and Kun-Tai Cho Abstract This research is to augment the intelligent

More information

College Readiness LINKING STUDY

College Readiness LINKING STUDY College Readiness LINKING STUDY A Study of the Alignment of the RIT Scales of NWEA s MAP Assessments with the College Readiness Benchmarks of EXPLORE, PLAN, and ACT December 2011 (updated January 17, 2012)

More information

ON-LINE MONITORING OF POWER PLANTS

ON-LINE MONITORING OF POWER PLANTS ON-LINE MONITORING OF POWER PLANTS Dr. Hans-Gerd Brummel Siemens Power Generation (PG), Huttenstrasse 12-16, 10553 Berlin, Germany Phone: +49 30 3464 4158, E-mail: hans-gerd.brummel@siemens.com Table of

More information

Simple Predictive Analytics Curtis Seare

Simple Predictive Analytics Curtis Seare Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use

More information

Five Essential Components for Highly Reliable Data Centers

Five Essential Components for Highly Reliable Data Centers GE Intelligent Platforms Five Essential Components for Highly Reliable Data Centers Ensuring continuous operations with an integrated, holistic technology strategy that provides high availability, increased

More information

Tutorial 5: Hypothesis Testing

Tutorial 5: Hypothesis Testing Tutorial 5: Hypothesis Testing Rob Nicholls nicholls@mrc-lmb.cam.ac.uk MRC LMB Statistics Course 2014 Contents 1 Introduction................................ 1 2 Testing distributional assumptions....................

More information

SYSTEMS, CONTROL AND MECHATRONICS

SYSTEMS, CONTROL AND MECHATRONICS 2015 Master s programme SYSTEMS, CONTROL AND MECHATRONICS INTRODUCTION Technical, be they small consumer or medical devices or large production processes, increasingly employ electronics and computers

More information

MODELBASED DIAGNOSTICS, MAINTENANCE ON DEMAND AND DECISION SUPPORT ON BOILERS

MODELBASED DIAGNOSTICS, MAINTENANCE ON DEMAND AND DECISION SUPPORT ON BOILERS MODELBASED DIAGNOSTICS, MAINTENANCE ON DEMAND AND DECISION SUPPORT ON BOILERS Erik Dahlquist 1), Björn Widarsson 1) 2) and Anders Avelin 1) 1)Mälardalen University, Västerås, Sweden 2) Fjärrvärmebyrån

More information

Data Mining mit der JMSL Numerical Library for Java Applications

Data Mining mit der JMSL Numerical Library for Java Applications Data Mining mit der JMSL Numerical Library for Java Applications Stefan Sineux 8. Java Forum Stuttgart 07.07.2005 Agenda Visual Numerics JMSL TM Numerical Library Neuronale Netze (Hintergrund) Demos Neuronale

More information

Performance evaluation of Web Information Retrieval Systems and its application to e-business

Performance evaluation of Web Information Retrieval Systems and its application to e-business Performance evaluation of Web Information Retrieval Systems and its application to e-business Fidel Cacheda, Angel Viña Departament of Information and Comunications Technologies Facultad de Informática,

More information

ASSURING THE QUALITY OF TEST RESULTS

ASSURING THE QUALITY OF TEST RESULTS Page 1 of 12 Sections Included in this Document and Change History 1. Purpose 2. Scope 3. Responsibilities 4. Background 5. References 6. Procedure/(6. B changed Division of Field Science and DFS to Office

More information

Find what matters. Information Alchemy Turning Your Building Data Into Money

Find what matters. Information Alchemy Turning Your Building Data Into Money Find what matters Information Alchemy Turning Your Building Data Into Money version 1.1 Feb 2012 CONTENTS Information Alchemy Transforming Data Into Value... 2 How Does My Building Really Perform?... 2

More information

Fuzzy Knowledge Base System for Fault Tracing of Marine Diesel Engine

Fuzzy Knowledge Base System for Fault Tracing of Marine Diesel Engine Fuzzy Knowledge Base System for Fault Tracing of Marine Diesel Engine 99 Fuzzy Knowledge Base System for Fault Tracing of Marine Diesel Engine Faculty of Computers and Information Menufiya University-Shabin

More information

Liquids Pipeline Leak Detection and Simulation Training

Liquids Pipeline Leak Detection and Simulation Training Liquids Pipeline Leak Detection and Simulation Training April 2010 / White paper Make the most of your energy Summary Executive summary... p 1 Introduction... p 2 CPM methods for pipeline leak detection...

More information

Anomaly Detection and Predictive Maintenance

Anomaly Detection and Predictive Maintenance Anomaly Detection and Predictive Maintenance Rosaria Silipo Iris Adae Christian Dietz Phil Winters Rosaria.Silipo@knime.com Iris.Adae@uni-konstanz.de Christian.Dietz@uni-konstanz.de Phil.Winters@knime.com

More information

Getting insights about life cycle cost drivers: an approach based on big data inspired statistical modelling

Getting insights about life cycle cost drivers: an approach based on big data inspired statistical modelling Introduction A Big Data applied to LCC Conclusion, Getting insights about life cycle cost drivers: an approach based on big data inspired statistical modelling Instituto Superior Técnico, Universidade

More information

Integrated Resource Plan

Integrated Resource Plan Integrated Resource Plan March 19, 2004 PREPARED FOR KAUA I ISLAND UTILITY COOPERATIVE LCG Consulting 4962 El Camino Real, Suite 112 Los Altos, CA 94022 650-962-9670 1 IRP 1 ELECTRIC LOAD FORECASTING 1.1

More information

F CUS your recruitment dollars on top scientists and engineers.

F CUS your recruitment dollars on top scientists and engineers. F CUS your recruitment dollars on top scientists and engineers. PHYSICS TODAY Recruitment Products and Rates 2016 WHY PHYSICISTS? Physicists, as well as scientists and engineers with training in first

More information

Software and Hardware Solutions for Accurate Data and Profitable Operations. Miguel J. Donald J. Chmielewski Contributor. DuyQuang Nguyen Tanth

Software and Hardware Solutions for Accurate Data and Profitable Operations. Miguel J. Donald J. Chmielewski Contributor. DuyQuang Nguyen Tanth Smart Process Plants Software and Hardware Solutions for Accurate Data and Profitable Operations Miguel J. Bagajewicz, Ph.D. University of Oklahoma Donald J. Chmielewski Contributor DuyQuang Nguyen Tanth

More information

CALIBRATION PRINCIPLES

CALIBRATION PRINCIPLES 1 CALIBRATION PRINCIPLES After completing this chapter, you should be able to: Define key terms relating to calibration and interpret the meaning of each. Understand traceability requirements and how they

More information

DEALING WITH THE DATA An important assumption underlying statistical quality control is that their interpretation is based on normal distribution of t

DEALING WITH THE DATA An important assumption underlying statistical quality control is that their interpretation is based on normal distribution of t APPLICATION OF UNIVARIATE AND MULTIVARIATE PROCESS CONTROL PROCEDURES IN INDUSTRY Mali Abdollahian * H. Abachi + and S. Nahavandi ++ * Department of Statistics and Operations Research RMIT University,

More information

Least Squares Estimation

Least Squares Estimation Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN-13: 978-0-470-86080-9 ISBN-10: 0-470-86080-4 Editors Brian S Everitt & David

More information

Getting Correct Results from PROC REG

Getting Correct Results from PROC REG Getting Correct Results from PROC REG Nathaniel Derby, Statis Pro Data Analytics, Seattle, WA ABSTRACT PROC REG, SAS s implementation of linear regression, is often used to fit a line without checking

More information

A THEORETICAL COMPARISON OF DATA MASKING TECHNIQUES FOR NUMERICAL MICRODATA

A THEORETICAL COMPARISON OF DATA MASKING TECHNIQUES FOR NUMERICAL MICRODATA A THEORETICAL COMPARISON OF DATA MASKING TECHNIQUES FOR NUMERICAL MICRODATA Krish Muralidhar University of Kentucky Rathindra Sarathy Oklahoma State University Agency Internal User Unmasked Result Subjects

More information

An introduction to OBJECTIVE ASSESSMENT OF IMAGE QUALITY. Harrison H. Barrett University of Arizona Tucson, AZ

An introduction to OBJECTIVE ASSESSMENT OF IMAGE QUALITY. Harrison H. Barrett University of Arizona Tucson, AZ An introduction to OBJECTIVE ASSESSMENT OF IMAGE QUALITY Harrison H. Barrett University of Arizona Tucson, AZ Outline! Approaches to image quality! Why not fidelity?! Basic premises of the task-based approach!

More information

Knowledge Discovery from patents using KMX Text Analytics

Knowledge Discovery from patents using KMX Text Analytics Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers

More information