SureSense Software Suite Overview

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1 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 Does Expert Microsystems SureSense and Heath Monitoring (PHM) software detects and diagnoses failures before they can disable equipment and sensors used in performance critical systems. The software transforms an avalanche of data into actionable decisions for critical system events impacting system operation, safety and reliability. Expert Microsystems (EM) built the SureSense PHM software with the philosophy that One Size Does Not Fit All. Each customer asset has unique issues and challenges that often do not fit readily into other rigid and fixed software solutions. EM s modular plug in framework, based on patented Mode Partitioning and Calibration algorithms in combination with EM s highly advanced Path Classification and (PACE) prognostic models, offers the most cost effective, accurate and flexible PHM solution available. The monitored system can be any source of observable data, such as a machine, a human being, a business process, or a computer network. For example, SureSense can accurately distinguish between malicious cyber attacks and routine firewall and intrusion detection alerts so that prompt actions can be taken for bona fide threats. SureSense is the only PHM software that provides fully integrated patented prognostic capabilities in combination with a flexible Java based architecture to accurately predict asset remaining useful life. SureSense PHM technology uses four principle steps, these being Prediction, Detection, Diagnosis and Prognosis. The Prediction step involves the use of one or more models describing the asset when it is operating correctly. The prediction model is used to estimate the expected values of the observed data from the asset. The Detection step compares observed data values to their expected values to determine the pattern of agreement or disagreement between the observed and expected parameters. The Diagnosis step correlates this pattern of agreement or disagreement with the most likely normal or abnormal state of the asset. The Prognosis step uses this evolving diagnosis and condition information to determine the probable remaining useful life of the monitored asset. SureSense ally Creates and Calibrates Monitoring s, ally Detects and Diagnoses Faults and ally Determines Remaining Useful Life for Critical Assets Calibration/Updating Process Acquire ing SELECT Training CALIBRATE System CREATE On-Line On-Line & Health Management Process Acquire Asset Operating PREDICT DETECT DIAGNOSE System Behavior with On-LIne Differences Between and Determine Significance of Differences PROGNOSE Determine Remaining Useful Life Warn if Asset Fault is Detected Warn if RUL is Below Minimum REMEDY Take Corrective Action

2 SureSense The SureSense PHM software suite consists of a flexible framework architecture, sold under the Diagnostic Monitoring Studio trademark, which is populated with four main types of modules: Diagnostic Monitoring Studio State Prediction Module Fault Detection Module Diagnostic Module Life Module In addition to these modules, SureSense includes a universal data acquisition interface, a graphical user interface, and a scientific data visualization and plotting package. The Diagnostic Monitoring Studio (DMS) is the backbone of the SureSense suite. It provides the framework to plug in customer proprietary models and data sources as well as EM s proprietary modules. The DMS has many unique features that provide a compelling advantage over competing products. EM s open, building block approach greatly reduces PHM system design, development and deployment time. It also allows SureSense to connect to any source of data in any format. This is a feature that competing PHM products do not offer. SureSense Uses a Flexible Architecture and Technologies to Provide Unmatched Accuracy, Reduced Deployment Time and Maximum Cost Effectiveness VALIDATION PREDICTION Diagnostic Monitoring Studio Universal Framework Equipment & System s s SureSense or er Provided Plug-in Modules

3 SureSense The DMS uses patented Mode Partitioning algorithms for rapid and accurate modeling of complex systems and to assure extremely low false alarm rates. It also provides and Calibration features that dramatically reduce deployment time and cost of model maintenance as an asset ages or is subject to seasonal changes. Finally, the framework has Automated Uncertainly technology to assure very high quality, quantitative decision analysis. The DMS is written in the platform neutral Java language in contrast to the platform specific languages used by other PHM software providers. This is significant because it allows customer tailored software to be quickly interfaced with SureSense as plug in modules. Additionally, Java-based solutions have inherent advantages relative to competing approaches for security and network distributed deployment. State Prediction Module Equipment & System s s SureSense offers more prediction capabilities than any competing PHM product. SureSense comes with a comprehensive library of both analytical and pattern recognition model types to predict the behavior of an asset and also provides the ability to integrate customer proprietary models. The best solution for our customer s unique state prediction challenges can be easily selected, verified and quickly deployed using our DMS framework. EM s Advanced Pattern Recognition (APR) models learn the behavior of an asset automatically. The most advanced APR model available is EM s (ESEE). This modeling module uses patented self-calibrating, high dimensional, autoassociative kernel regression technology to provide fast and stable models. Other modeling modules currently available in SureSense include: classical autoassociative kernel regression, multivariate state estimation technique (MSET), nonparametric fuzzy inference, parity space averaging, functional relation and time series averaging libraries, time series autoregression and probability scoring. principle analytical models are also available for many types of circuits, turbine engines and rocket engines. Selection of the best modeling technique is straightforward and is based on the type of signal and data capture at a customer s site and the physical relationships between the data parameters for the operating assets. Fault Detection Module Equipment & System s s Comparing the observed signal values from an asset to expected signal values enables very early detection of developing faults. SureSense can be configured to use both classical rule based detection logic and EM s advanced probability based models, either separately or in combination. Rule based fault detection models, such as threshold limits, are most useful for detecting large magnitude errors that exceed normal expected behavior. based models analyze the residual difference between the expected signal values and the observed signal values and dynamically evaluate the probability density functions for the residuals to determine if the changes are statistically significant. The most advanced probability based model available is EM s patented (ASP) technology, which provides the optimum balance between high sensitivity and negligible false alarms. Other fault detection capabilities currently available in SureSense include: range/noise/change limit detectors, statistical process control limits, missing value detectors and classical sequential probability detectors.

4 SureSense Diagnostic Module Equipment & System s s When one or more asset signals produce a fault indication, EM s decision module is used to determine the cause for the For example, a fault could be caused by a malfunctioning sensor, an electrical short, or equipment that is malfunctioning. Each of these causes has a unique pattern of fault indications and probability of occurrence. The SureSense (BBN) diagnostic decision module can be configured to pinpoint the most likely cause of the fault whereas EM s unique Unmodeled Condition Detector automatically builds a diagnostic model that can reliably distinguish sensor failures from other types of faults. The BBN diagnostic decision module provides the most accurate and maintainable automated reasoning capability available thereby providing a distinct advantage over rule-based decision approaches used in some of our competitor s products. SureSense makes the BBN s powerful decision modeling capability highly automated and intuitive. Prognosis Prediction Module Equipment & System s s SureSense is the only PHM software that offers a tightly integrated prognosis prediction module within a flexible open architecture asset monitoring system. The prognostic module includes our patented Path Classification and (PACE) technology, which uses non-parametric regression over multiple degradation path models to predict remaining useful life with unparalleled accuracy. Other prognosis modules include a Proportional Hazards Module, which employs usage and stress variables to predict remaining useful life and a General Path Module that uses the value of a degradation parameter to predict remaining life. These modules can be run simultaneously to allow prognostic models appropriate for the beginning of an asset s life to be compared to models appropriate near the end of an asset s useful life. Our unique multi-model approach assures highly accurate remaining life predictions for the monitored asset. SureSense Applications Originally developed with sponsorship from NASA and the U.S. Departments of Defense, Energy, and Homeland Security, SureSense capabilities have been proven for a broad range of high value applications including: Conventional & Nuclear Power Generation Clean Technology Chemical and Process Plants Commercial and Military Turbine s Rocket Propulsion and Spacecraft Battery and Stored Energy Systems Cyber Security Servers and s Sensor Calibration and Validation OEM Product Embedded Monitoring SureSense has been tested and validated against stringent military and government requirements. The flexibility and accuracy of SureSense has been successfully demonstrated for Space Shuttle Main telemetry data validation, Joint Strike Fighter battery subsystem monitoring, F/A-22 turbine engine ground test verification, SCADA cyber security assurance and for instrument and equipment monitoring at multiple nuclear power plants.

5 SureSense SureSense Installation SureSense Benefits Configuring SureSense s rich feature set for a specific application is straightforward and consists of the following tasks: Identify the goals of the PHM deployment Assess the available data for the asset Select the most useful data for modeling Select and install the optimum modeling technology to provide the most accurate and cost effective solution Installation can typically be completed in less than 30 days. Installation is accomplished in four easy steps: Reduce plant maintenance costs by 25% Reduce unplanned breakdowns by 70% Reduce instrument calibration costs by 90% Accurately predict asset remaining useful life Reduce false alarm related costs Rapid to deploy and easy to use ized for your unique requirements Compatible with all operating systems Compatible with your legacy technologies Eliminate bad data for improved control Embed self-diagnostics in OEM products Shorten OEM product development time 1 Connect Sources Pull data from existing customer data sources(s) 2 Calibrate SureSense s Learn data that is normal for the monitored asset 3 Validate Sensitivity and Accuracy ally characterize and optimize models using built-in tools 4 Roll out Software and Monitor Begin active monitoring of your critical assets SureSense has user friendly interfaces and presents information in concise and easy to understand formats. The user interface is customizable to display alarms for all types of systems using an intuitive mouse activated system view. The extensive plotting capabilities are context sensitive for items selected in the system display and are configurable to meet all types of data visualization needs. technology, flexibility, and ease of installation and use make SureSense the leader in PHM software products.

6 SureSense Offers the Most Comprehensive s to Ensure the Optimum Solution Is Implemented for Each Unique er PHM Requirement System Architecture Prediction Fault Detection Diagnosis Prognosis SureSense Features Flexible Java Foundation Mode Partitioning Algorithms Calibration Capabilities Automated Uncertainty Static Calibration Dynamic Calibration (ESEE) Autoassociative Kernel Regression (AAKR) State Technique (MSET) Non Parametric Fuzzy Inference (NFIS) Parity Space Averaging Relation Function Library Time Series Average Library Algorithms Time Series Autoregression s Range Limit Detector Noise Limit Detector Delta Limit Detector Statistical Process Control (SPC) Module Missing Value Detector Classical Detector Detector Unmodeled Condition Detector Proportional Hazards General Path Path Classification and Comments Most flexible PHM framework architecture available Significantly reduces false alarm rate calibration that updates as an asset ages Highly accurate technique for determining model uncertainty ally selects data for model calibration ally acquires new data for adaptive model calibration Highly accurate autoassociative, multivariate kernel regression model Classic multivariate kernel regression model By Argonne National Lab, available in SureSense and from others Autoassociative, multivariate fuzzy prediction model Consistency weighted average for redundant signals Large library of 2D and 3D analytical models Simple and composite averages of time series data Univariate score measures probability density function shift Classic univariate autoregression model For example, battery circuit, turbine engine and rocket engine models High and low value limits ized high & low standard deviation methods Positive and negative step change limits Classic SPC mean and range limits Null value or assigned constant Detect mean or variance change using classical method Detect mean or variance change using highly accurate method nodes with automated connections to fault detectors determination of data fault versus equipment fault Usage and stress data used to predict remaining life Value of a degradation indicator used to predict remaining life Non-parametric prediction using multiple degradation path models Expert Microsystems, Inc Country Trail Drive, 1 Orangevale, CA

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