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 and Gas Jenkinsville, South Carolina rrusaw@scana.com Eddie Davis Edan Engineering Corporation Vancouver, Washington edavis@edanengineering.com Dr. Ramesh Shankar Electric Power Research Institute Charlotte, North Carolina rshankar@epri.com KEYWORDS Condition monitoring, diagnostic monitoring, signal validation, sensor calibration, industrial automation, process control, process surveillance, smart systems ABSTRACT To assure the continued safe, reliable and efficient operation of a nuclear power plant, it is essential that accurate online measurement information is available to the plant operators, engineering and maintenance personnel. This paper describes new online signal validation and predictive condition monitoring software that uses innovative artificial intelligence techniques to detect instrument degradation and perform system diagnostics with higher accuracy and faster response time than prior art techniques. The software uses a statistical modeling technique to learn a high fidelity model of an asset, such as a process or set of equipment, from a sample of its normal operating data. Once built, the model provides an accurate estimate for each observed signal given a new data observation from the asset. Each estimated or virtual signal is compared to its actual signal counterpart using a highly sensitive fault detection procedure to statistically determine whether the actual signal agrees with the learned model. Online signal validation is applicable to power plant monitoring and control systems where time-critical safety or control functions depend on sensor input, or unexpected process interruptions due to sensor and equipment failures or false alarms are unsafe or uneconomical. Online signal validation improves process quality and efficiency by ensuring that closed loop control is performed using good sensor data. Online signal validation increases system availability and decreases system maintenance cost by optimizing performance and enabling instrumentation calibration and equipment maintenance using condition based criteria rather than time-in-service criteria. 1
In this paper, we discuss the Multivariate State Estimation Technique (MSET) and associated fault detection algorithms used to perform online signal validation and predictive condition monitoring. Application examples and benefits of online monitoring for nuclear power plant surveillance systems are presented. Current capabilities and planned enhancements to the algorithms and software are discussed. INTRODUCTION Expert Microsystems has developed the SureSense Multivariate State Estimation Studio signal validation software [1] to meet the unique and specific needs of the electric power-generating industry. The software ensures measurement signal integrity and thereby improves operating performance, reduces downtime, and can lower operating and maintenance cost for a wide variety of powergenerating applications. MSET was originally developed by Argonne National Laboratory for nuclear power applications where plant downtime can cost utilities and their constituents on the order of $1- million a day. MSET is a statistical modeling technique that learns a high fidelity model of an asset from a sample of its normal operating data. Once built, the software model provides an accurate estimate for each observed signal given a new data observation from the asset. Each estimated signal is compared to its actual signal counterpart using a highly sensitive fault detection procedure to statistically determine whether the actual signal agrees with the learned model or alternatively is indicative of a process anomaly, sensor data quality problem, or equipment problem. Over the last two years, MSET technology has been the subject of several successful trials using actual nuclear power plant data. These trials culminated in the July 2000 release by the U.S. Nuclear Regulatory Commission (NRC) of a safety evaluation report (SER) for the use of online monitoring as a means of extending the calibration intervals of safety-related instrumentation [2]. The MSET software described herein is configured to detect instrument degradation and perform system diagnostics with higher accuracy and faster response time than prior art techniques. This capability can substantially improve equipment availability and improve plant uptime. DIAGNOSTIC PROCEDURE OVERVIEW The software enables process engineers to define a diagnostic model for their process or equipment, optimize the design, and then automatically generate the software that performs the online diagnostic function. The model development process is accomplished using a graphical mouse-driven set of tools and a proven methodology. This combination of tools and methodology permits complex diagnostic systems to be developed in a very short amount of time. The software s validation algorithm combines MSET parameter estimators with sophisticated decision logic for a solution that provides excellent real-time performance, well-defined error rates, and is scaleable to validate any number of system signals. MSET provides a high fidelity estimate of the expected response of the system sensors by using advanced pattern recognition techniques to measure the similarity between sensor signals within a learned operational domain. The learned patterns or relationships among the signals are used to identify the operating state that most closely corresponds with the current measured set of signals. By quantifying the relationship between the current and 2
learned states, MSET estimates the current expected response of the system sensors. The mathematical foundations of the MSET algorithms are well described in the literature [3]. Referring to Figure 1, the overall diagnostic procedure consists of a training step and a monitoring step. The training step is used to characterize the monitored asset, such as a process or item of equipment, using historical operating data. There are two important attributes of the historical operating data used to train an MSET model of the asset. First, the data should contain all modes and ranges of operation that are to be considered normal operation of the asset. Second, the data should not contain any operating anomalies, sensor failures or equipment failures that would be considered abnormal operation of the asset. These criteria are prerequisites for the learned MSET model to fully characterize normal operation of the asset. After a comprehensive and error-free set of training data has been assembled, the MSET training algorithms are used to build an MSET model of the asset. The training procedure evaluates the training data and selects a subset of the training data observations that are determined to best characterize the asset s normal operation. First, the observations containing the minimum and maximum observed value for each included signal are selected. The minimum and maximum observed values define the valid operating range of the MSET model for the asset. The set of minimum and maximum observed values also define the smallest possible MSET model of the asset. Next, the training procedure attempts to select a number of additional points that best characterize the asset s operating states between the minimum and maximum limits. The procedure used to fill in the additional states is performed by first ordering the training data observations using a statistical method based on the Euclidean norms of the data observations. The procedure then selects observations at equal intervals to fill the MSET model with the user-specified number of operating data examples. The training step results in a stored MSET model of the asset that may be used in the monitoring step to estimate the expected values of the signals. In the monitoring step, a new observation of the asset signals is first acquired. Again referring to Figure 1, this observation is used in conjunction with the previously trained MSET model to estimate the expected values of the signals. The estimation procedure is accomplished by comparing the new observation to the previously learned examples. A weighting method is used to produce the estimate by combining the example data values. Those examples most similar to the current observation are heavily weighted while those that are dissimilar are negligibly weighted. Similarity between the current observation and the learned examples is computed using sophisticated multivariable pattern matching techniques. The weighted combination of the most similar learned examples is used to compute the estimated signal values given the current observed signal values. The MSET technique provides an extremely accurate estimate of sensor signals, with error rates that are typically 1% to 2% of the standard deviation of the input signal, which is excellent. 3
Training Data Calibrate Model Asset Acquire Data Online Model Parameter Estimation Training Monitoring Fault Detection Alarm or Control Action No Fault Found? Yes FIGURE 1. DIAGNOSTIC PROCEDURE OVERVIEW The difference between a signal s predicted value and its directly sensed value is termed a residual. The residuals for each monitored signal are used as the indicator for sensor and equipment faults. Instead of using simple thresholds to detect fault indications (i.e., declaring a fault when a signal s residual value exceeds a preset threshold), the software s fault detection procedure employs a proprietary Bayesian Sequential Probability (BSP) hypotheses test technique to determine whether the residual error value is uncharacteristic of the learned process model and thereby indicative of a sensor or equipment fault. The BSP algorithm improves the threshold detection process by providing more definitive information about signal validity using statistical hypothesis testing. The BSP technique allows the user to specify false alarm and missed alarm probabilities, allowing control over the likelihood of false alarms or missed detection. The BSP technique is a superior surveillance tool because it is sensitive not only to disturbances in the signal mean, but also to very subtle changes in the statistical quality (variance, skewness, bias) of the signals. For sudden, gross failures of a sensor or item of equipment, the BSP procedure will annunciate the disturbance as fast as a conventional threshold limit check. However, for 4
slow degradation, the BSP procedure can detect the incipience or onset of the disturbance long before it would be apparent with conventional threshold limits. In general, the BSP procedure is accomplished by first establishing the expected distribution of the residual values when the asset is operating normally. This step is accomplished in conjunction with the MSET model training procedure. Once an MSET model is trained by learning examples from the training data, the remaining (unselected) training data observations are run through the model in order to characterize the expected distribution of the residual values. Having characterized the expected distribution of the residual values when the asset is operating normally, the BSP procedure may be used to detect those conditions that deviate from the learned MSET model. In operation, a time series of residual values are evaluated to determine whether the series of values is characteristic of the expected distribution or alternatively of some other specified distribution. Four possible fault-type distributions are considered in the current software. These are: 1) the residual mean value has shifted high; 2) the residual mean value has shifted low; 3) the residual variance value has increased; and 4) the residual variance value has decreased. The sensitivity of the BSP procedure when selecting between the expected (null-type) distribution and a fault type distribution is primarily established by a user-configurable setting termed the system disturbance magnitude. The system disturbance magnitude controls the crossover point at which a disturbance in the residual values is deemed uncharacteristic of the normal operating states of the monitored asset. The definitive fault alarm decision is made using a conditional probability analysis of a series of BSP fault detection results in order to reduce the potential for single observation false alarms. The conditional probability technique improves on the conventional multi-cycle voting approach (e.g., fail on N fault indications out of the last M observations) by allowing the user to more explicitly control the statistical confidence level used in the fault alarm decision. MSET techniques have been successfully applied in a number of reliability critical applications, including monitoring of Space Shuttle s Main Engine sensors [4], military gas turbine engine sensors, industrial process equipment, high-performance computers, and nuclear power plant sensors [5,6]. POWER PLANT APPLICATIONS The design of an online diagnostic model begins with the user s definition of the signal parameters to be processed by the model. These will include all signals required for diagnostic processing, either as targets of validation or as collaborating data. A typical steam system model is illustrated in Figure 2. During online monitoring, the software displays the current signal values using data boxes that visually highlight warning and fault conditions using yellow (warning) and red (fault) backgrounds for the affected signal. The illustration in Figure 2 highlights a fault condition detected in the monitored signal 1-L-06. The display is mouse-active enabling the user to quickly drill down to retrieve detailed monitoring results, plot signals and model-generated data, or view model design information for any signal or other model element. The model represented in Figure 2 is based on actual data from an operating nuclear power plant. The model was trained at the software s default settings using data from the initial months in the plant s fuel 5
cycle. Several months into the fuel cycle, a drift failure in steam generator level signal 1-L-06 occurred. Referring to Figure 3, this drift event is detected by the software at the point shown. The drift error detection results for signal 1-L-06 illustrate the excellent sensitivity of the software for detecting sensor calibration drift and other common operating problems. The data plot shows the actual signal values in blue and the MSET-estimated values in red. The highly expanded scale of the dependent axis in Figure 3 makes the drift error obvious in hindsight as the problem progresses. However, the software s automated fault detection algorithms are clearly able to detect the drift in online operation more quickly than a skilled human observer. For comparison, the observed and estimated values over the training data set are shown in Figure 4. The excellent ability of the MSET algorithm to track normal trends and fluctuations in the actual signal data is readily apparent in Figure 4. The ability of the software to accurately estimate online signals from a nuclear power plant is further illustrated in Figure 5 and Figure 6. Figure 5 presents the data from a redundant steam generator level sensor during the drift event that is affecting signal 1-L-06 in Figure 3. The redundant signal 1-L-07 is accurately estimated by the MSET model despite the presence of the error in signal 1-L-06. Figure 6 presents the data for the steam generator pressure, signal 1-P-02, and illustrates the model s ability to accurately track the complicated characteristics of the monitored signals. FIGURE 2. TYPICAL STEAM SYSTEM MODEL 6
FIGURE 3. DRIFT ERROR DETECTION FOR SIGNAL 1-L-06 FIGURE 4. TRAINING DATA BASIS FOR SIGNAL 1-L-06 7
FIGURE 5. REDUNDANT SIGNAL 1-L-07 DURING DRIFT FAILURE OF 1-L-06 FIGURE 6. STEAM GENERATOR PRESSURE SIGNAL 1-P-02 8
POWER PLANT BENEFITS Predictive condition monitoring using online signal validation can provide management and plant operators with early indications of deteriorating sensors and equipment. With advanced warning of impending failures, management can take corrective action during periods of planned downtime, increasing the productive availability of the monitored equipment. The software s ability to detect a signal fault when it is only a fraction of the normal operating range and noise band of the signal provides a breakthrough capability for preventing unscheduled equipment downtime. Most industrial equipment provides a peak system availability of 85% to 95%. The downtime associated with industrial equipment represents a significant, and often hidden, cost. Equipment downtime for a power generating plant has a direct and immediate impact on revenues since electricity cannot be stockpiled as a buffer to protect against short-term outages. If unable to generate electricity, a generating plant will absorb lost revenues, along with the high costs of unplanned repair and maintenance. If the plant has contractual commitments to provide power, the costs of purchasing electricity on the spot market may further erode revenues. In an effort to maximize equipment availability, many generating plants have implemented intensive preventative maintenance programs. However, according to the industry research firm ARC Advisory Group, half of all preventative maintenance is unnecessary and serves only to increase maintenance costs. Predictive condition monitoring offers a practical solution to optimize a generating plant s operations and maintenance (O&M) effectiveness and minimize costs. CURRENT WORK This software is currently the focus of a significant body of work targeted at further advancing the stateof-the-art in online signal validation and diagnostic monitoring. The US Department of Energy is funding upgrades to the software to improve the level of modeling automation and develop other ease of use features to enable wide spread use throughout the power industry by requiring less algorithm expertise for successful development of effective monitoring systems. The US Air Force is funding deployment of the software into test facilities supporting gas turbine engine development with numerous new technical capabilities and performance enhancements planned. The State of California is funding the implementation an Internet-enabled client-server version of the software. EPRI and Expert Microsystems are working together to implement software upgrades and applications studies specifically supporting the requirements of the power-generating industries, and most notably the requirements of the nuclear power industry for primary side instrument calibration reduction [7]. CONCLUSIONS SureSense predictive condition monitoring software enables significantly improved asset management for power generating facilities. The software s ability to provide very early fault detection enables highly effective predictive maintenance of plant equipment and prevents forced outages and other unscheduled downtime. In the increasing competitive power generation business, this capability can provide generating plants with a significant advantage. 9
ACKNOWLEDGEMENTS The support of EPRI Power Production Directorate and its member utilities in the performance of this work is gratefully acknowledged. REFERENCES 1 E. Davis and R.L. Bickford, SureSense Multivariate State Estimation Studio Users Guide, Version 1.3, EPRI, Palo Alto, California, January 2002. 2 E. Davis, D. Funk, D. Hooten, and R. Rusaw, On-Line Monitoring of Instrument Channel Performance, TR-104965-R1 NRC SER, EPRI, Palo Alto, California, September 2000. 3 R.M. Singer, K.C. Gross, J.P. Herzog, R.W. King, and S.W. Wegerich, Model-Based Nuclear Power Plant Monitoring and Fault Detection: Theoretical Foundations, Proc. of the 9th International Conference on Intelligent Systems Applications to Power Systems, Seoul, Korea, July 1997. 4 R.L. Bickford, et al, MSET Signal Validation System for Space Shuttle Main Engine, Final Report, NASA Contract NAS8-98027, August 2000. 5 K.C. Gross, R.M. Singer, S.W. Wegerich, J.P. Herzog, R. VanAlstine, and F. Bockhurst, Application of a Model-Based Fault Detection System to Nuclear Plant Signals, Proc. of the 9th International Conference on Intelligent Systems Applications to Power Systems, Seoul, Korea July 1997. 6 J.P. Herzog, S.W. Wegerich, and K.C. Gross, MSET Modeling of Crystal River-3 Venturi Flow Meters, Proc. ASME/JSME/ SFEN 6th International Conference on Nuclear Engineering, San Diego, California, May 1998. 7 E. Davis, et al, On-Line Monitoring at Nuclear Power Plants Results From the EPRI On-Line Monitoring Implementation Project, Proc. of POWID 2002, San Diego, California, June 2002. 10