Use of Autoassociative Neural Networks for Signal Validation

"Use of Autoassociative Neural Networks for Signal Validation", by J. Wesley Hines, Darryl J. Wrest and Robert E. Uhrig, published in the proceeding of NEURAP 97 Neural Network Applications, Marseille, France, March -3, 997. Use of Autoassociative Neural Networks for Signal Validation J. Wesley Hines and Robert E. Uhrig Nuclear Engineering Department The University of Tennessee Knoxville, Tennessee 37996 hines@utkux.utk.edu ruhrig@utk.edu Darryl J. Wrest Systems Health Management Group Honeywell Technology Center Minneapolis, Minnesota 5548 dwrest@htc.honeywell.com Abstract Recently, the use of Autoassociative Neural Networks (s) to perform on-line calibration monitoring of process sensors has been shown to be not only feasible but practical. This paper summarizes the results of applying s to instrument surveillance and calibration monitoring at Florida Power Corporation s Crystal River #3 Nuclear Power Plant and at the Oak Ridge National Laboratory High Flux Isotope Reactor. In both cases sensor drifts are detectable at a nominal level of.5% of the instrument s full scale range. This paper will discuss the selection of a five layer neural network architecture, a robust training paradigm, the input selection criteria, and a retuning algorithm.. Introduction Maintenance techniques have drastically changed over the past few years. Companies are experiencing increased competitiveness and are applying new information processing technologies to reduce expenses. The original maintenance philosophy was one of corrective maintenance and could be stated as if it s not broke, don t fix it. Maintenance engineers soon found that preventive maintenance could reduce maintenance expenses and down time. Most competitive manufacturers now use periodic maintenance which requires maintenance tasks be performed on a time based schedule. None of these philosophies are optimal. Maintenance should be performed based on the condition of the equipment. This paper presents a condition based maintenance strategy for process sensors. Traditional approaches to sensor validation involve periodic instrument calibration. These calibrations are expensive both in labor and process down time. Many periodic sensor calibration techniques require the process shut down, the instrument taken out of service, and the instrument loaded and calibrated. This method can lead to damaged equipment, incorrect calibrations due to adjustments made under non-service conditions, and loss of product due to unnecessarily shutting down a process. While proper adjustment is vital to maintaining effective plant operation, less invasive techniques are available. As increased economic competitiveness necessitates streamlining plant operations, many maintenance groups are striving towards condition based maintenance rather than periodic, or worse yet, corrective maintenance. Changing calibration strategies to be condition based, requires instruments to be physically recalibrated only when their performance is degraded. Continuous monitoring of the instrument's calibration performance will allow plants to reduce the efforts necessary to assure the instrument is in calibration. Benefits of continuous sensor calibration monitoring include the reduction of unnecessary maintenance and more confidence in actual sensed parameter values. Reducing maintenance would result in cost savings and reduced outage times while a better knowledge of the actual state of the process could result in increased product quality and reduced equipment damage. The use of Autoassociative Neural Networks (s) for plant wide monitoring was developed by the University of Tennessee (UT) and reported in NUCLEAR

TECHNOLOGY. More recently, researchers at UT developed a sensor monitoring system for Florida Power Corporations Crystal River #3 nuclear power plant 2,3 and Oak Ridge national Laboratory s High Flux Isotope Reactor 4. Related nuclear work includes the monitoring of the Borssele Nuclear Power Plant using techniques 5. Similar work using s applied to chemical process systems have also been reported. 6,7,8 This paper summarizes some of these techniques and discusses some of the limitations of these methods. 2. Calibration Monitoring System The sensor calibration monitoring system is composed of four major components: an autoassociative neural network (), a statistical decision logic module (SPRT), a faulty sensor correction module, and a network tuning module. These modules work together to monitor the plant sensors for drift and gross failures. 2. Autoassociative Neural Network An autoassociative neural network is a network in which the outputs are trained to emulate the inputs over an appropriate dynamic range. Many plant variables that have some degree of coherence with each other constitute the inputs. During training, the interrelationships between the variables are embedded in the neural network connection weights. A robust training procedure is used to force the network to rely on the information inherent in the signals correlated with a specific sensor to estimate that specific sensor's value. As a result, any specific network output shows virtually no change when the corresponding input has been distorted by noise, faulty data, or missing data. This characteristic allows the to detect sensor drift or failure by comparing the sensor output, which is the network input, with the corresponding network estimate of the sensor value. Figure shows a sensor monitoring module for a group of four redundant sensors. Similar modules are constructed for monitoring groups of sensors whose measurements are correlated to some degree but not necessarily redundant. When a sensor that is input to the autoassociative network is faulty due to a drift or gross failure, the network still gives a valid estimate of the correct sensor value due to its use of information from other correlated sensors. The estimated sensor output (s n ') is then compared to the actual sensor output (s n ). The difference is called an error or a residual (r n ). The residual normally has a mean of zero and a variance related to the amount of noise in the sensor's signal. When a sensor is faulty, its associated residual's mean or variance changes. This can be detected with statistical decision logic. s s 2 s 3 s 4 Model s ` + r Σ s 2` + - r Σ 2 s 3` + - r Σ 3 s 4` - r 4 + Σ - Statistical Decision Logic Figure. Monitoring Module 2.2 Statistical Decision Logic Fault Hypothesis The decision logic module implements the sequential ratio probability test (SPRT) initially developed by Wald 9 and later used by Upadhyaya. This module uses the residual as the input and outputs the condition of the sensor. Rather than computing a new mean and variance at each sample time, the SPRT continuously monitors the sensor's performance by processing the residuals. This SPRT based method is optimal in the sense that a minimum number of samples are required to detect a fault existing in the signal. When a sensor is operating correctly, the residual should have a mean of zero, and a variance comparable to that of the sensor (due to the filtering characteristics of an ). If there is sensor drift, the residual mean shifts. Due to the shift in residual mean, the likelihood ratio increases. This ratio is a measure of how close the residual is to zero. If the likelihood ratio increases above a certain predefined boundary (user specified by false and missed alarm probabilities), the residuals are more likely to be from the faulted distribution than from the unfaulted distribution, and the sensor is classified as faulted. When the likelihood ratio is less than the boundary, the sensor is said to be good. If a sensor is determined to be faulty, the likelihood ratio is reset to zero and the calculation to determine the status of the sensor begins again. 2.3 Faulty Correction The statistical decision module continues to monitor a sensor output even after it has been determined to be faulty. While the sensor is faulted, the best estimate of the sensor value (the neural network output) can be used for input into control systems, for display to plant operators, or for other sensitive tasks. The best estimate also replaces the faulty sensor as input into the so that the monitoring of other sensors is not degraded. The actual sensor output is substituted back into the network when the fault has been cleared. This method always gives the operator access to the best estimate of the parameter, whether it is the unfaulted measured value or the estimated value.

2.4 Network Tuning The architecture used is a three hidden layer feedforward network proposed by Kramer 5. It consist of an input layer, 3 hidden layers, and an output layer. The first of the hidden layers is the mapping layer with dimension greater than the number of input/outputs. The second hidden later is called the bottleneck layer. The dimension of this layer is required to be the smallest in the network. The third hidden layer is called the demapping layer and has the same dimension as the mapping layer. Kramer points out that five layers are necessary for such nets in order to model non-linear processes. The decision to use this architecture and its implementation is discussed in reference 3. The three hidden layers form a "feature detection" architecture in which the bottleneck layer plays the key role in the identity mapping. The mapping layer maps from the input data space to the non-linear principle component space (bottleneck layer), and the demapping layer map from the non-linear principle component space to the data space (network output) corrected by the nonlinear principle components. This network learns the interrelationships between the variables during training. Although the training set should include samples from all plant operating regions, sometimes the operating state may change to one that was not included in the training set. This can be caused by component wear, cyclical changes, or changes in the plant configuration, among others. These changes would be detected by several residuals deviating significantly from their normal mean of zero. When this happens, the output of the is not reliable and the network must be retrained to operate under the new conditions. If only one residual changes, a sensor fault is hypothesized. Two methods of retraining have been investigated: history stack adaptation and retraining only the linear output layer weights 2. History stack adaptation is an adaptive training paradigm which simply adds new patterns to the training set and retrains the entire network. Several schemes give greater weight to newer patterns and drop old patterns out of the stack. Many researchers believe that the hidden layers of a neural network act as feature extractors and the linear output layer combines these features to provide a desired mapping. If the features do not change when a plant operating condition changes, then we can simply solve for the output layer weights that perform the desired mapping without retraining the entire network. This assumption seems to hold for small changes in operating conditions, so retraining only the output weights should always be attempted first. Retraining the entire network may be necessary for major changes in plant operating conditions when retraining the output weights does not result in satisfactory performance. Retraining only the linear output is very fast. In fact, we are not retraining the weights, we are using a least squares procedure to solve for the weights. Several methods of solving for the linear output weights exist including pseudoinverse methods which can cause numerical inversion problems, better methods use the LU or QR decompositions. The best method uses the singular value decomposition (SVD) 3 which uses the most relevant information to compute the weight matrix and discards unimportant information that may be due to noise. The SVD method is also used during the original network training and resulted in a 4x reduction of training time 4 over backpropagation with an adaptive learning rate and momentum. 2.5 System Integration and Implementation Figure 2 presents a SIMULINK 5 block diagram of the HFIR sensor monitoring system discussed in Section 4. This diagram shows the interrelations between the modules. Measured sensor data is input to the system sequentially and is initially processed by the correction module. If the sensor has not been determined to be faulty, the sensor value is used as input to the. In this figure, there are 3 sensors being monitored. The network produces an estimate of the individual sensor values. These values are compared to the actual values and residuals are formed. The residuals are fed to the SPRT based decision logic module and a decision on the status of the sensor is made. If the sensor is determined to be faulty, the correction module substitutes the estimated value for the sensor output and uses it as the input to the. If the sensors are fault free, the actual sensor outputs are used as inputs to the. The reset buttons on the left side of the figure allow the user to acknowledge sensor faults and to try to reset the input to the actual sensor output. This is useful to clear spurious alarms. An automatic mode allows the sensor output to be substituted back into the network whenever the fault clears. 3. Florida Power Corporation Example Data from Florida Power Corporation s Crystal River #3 Nuclear Power Plant consisted of 4 days of full power plant operation sampled every 2 minutes, resulting in 288 test patterns. A sensor calibration monitoring module was designed to monitor 22 critical plant sensors. Initially, a test was performed using plant data that contained several transients to verify that no false alarms occurred with error free plant data. Simulations were then performed that demonstrate the system's ability to detect drift as well as gross fault errors.

3. Drift Error Detection A drift error is defined as a slow rate of change in a signal's expected nominal value. To test the performance of the networks, both high and low drift faults of.% per day of the instruments maximum scale value were artificially created in each of the 22 sensor channels. Simulations were performed to see how soon the based sensor monitoring system could detect the fault with a minimum of false alarms. For each simulation, the time until the fault is first detected, the percent error of the drift (with respect to the full scale deflection of the signal) at the time of detection, and the number of false alarms in all the channels were recorded for both high and low drift fault scenarios. In each test case, faults were initiated at time zero. Figure 3 is a plot of a typical drift test cases in sensor R234 (reactor loop flow A). The top plot shows the actual drifting signal and the neural network estimate (note the network filtering), the middle plot shows the residual between the two, and the bottom plot shows the SPRT fault hypothesis index. In all test cases, no false alarms were recorded. Table. summarizes the results for the three typical sensors. It lists the computer point tag ID, the SPRT faulted mean value (twice the desired drift detection threshold), the calculated residual variance, and the percent (of maximum scale deflection) detected drift error. Computer Pt. (Tag ID) Table. Selected Drift Simulation Results Set SPRT faulted mean SPRT residual variance R234.35..4 R22.2.6.5 R225 7. 4. 2.39 % Detected drift error As the table shows, the system performed very well. The detection time generally depended on the amount of noise in the signal; the more noise, the higher the detection threshold. The average detection threshold for a low noise sensor, such as temperature, was approximately.2%. For a high noise sensor, such as pressure, the detection threshold was approximately 2.4%. On average, all sensor estimates performed equally well, despite of the fact that some were highly linearly correlated within the network (reactor temperature) and some had practically no linear correlation at all (pressurizer level). This equality was embedded into the networks by using a robust training method 3. Robust training forces each parameter in the network to rely on all the other parameters equally. 3.2 Gross Error Detection Gross faults are defined in this study as drastic sensor failures. A plant scenario would be a circuit that opens or shorts, where a complete loss of signal is encountered. Gross faults are simulated by failing the sensor to its maximum or minimum full scale deflection, representing gross fault "high", or gross fault "low" respectively. Depending on how "grossly" the signal fails, other sensor estimates may, or may not vary due to the loss of information. Experimental results have shown that a robust network can effectively compensate for a loss of approximately 25% of any one particular signal value. A gross fault can create false alarms in other channels due to the networks reliance on a select few parameters, but the residual levels are smaller than that of the failed sensor. For example, if sensor 4 fails low, then the residual for sensor 4 is around, while the other sensor's residuals are around. The use of two sets of SPRTs, one set with a low detection threshold and one set with a high detection threshold, can correctly identify the failed sensor 3. The use of the sensor correction module described in Section 2.3 replaces the faulty sensor with its estimate. This replacement of information restores the monitoring system's accuracy. All variables were tested under gross fault conditions. Each variable was failed high and low (max and min scale deflection values for each variable) with the time of detection and the number of false alarms in other channels recorded. Consider sensor 9 failed low, (R222 Reactor outlet pressure A). At time sample 5, the sensor drops from its median value of 23 PSIG to its minimum scale deflection of 7 PSIG (drop of approximately 4 PSIG), with an associated change in residual mean of approximately 4 PSIG. This greatly exceeds the faulted mean value of the SPRT, thus an alarm is initiated at time sample 5 (immediately). The residual of signal 6 (R224 Reactor outlet pressure B), which is monitored by the same network, changes slightly, but not enough to set off the SPRT. This is due to the less stringent faulted mean threshold of the "gross fault" SPRTs. The residuals of the remaining 2 sensors changed approximately ±, all less than their faulted mean setpoints. Thus the system accurately detected a gross fault in sensor 9 with no false alarms recorded in other channels. 4. HFIR Example Data from the Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR) was used to further test the sensor calibration monitoring system methodology. Fifty-six sensors that were sampled at two second intervals were divided into four groups based on

linear correlation coefficients and genetic algorithm selection. The secondary sensor group consisting of 3 sensors is used in this example. The linear correlation between these sensors ranged from ±. to ±.9. An was trained using 25 patterns from the first two days of operation. Decision logic alarm levels were set to give zero false alarms. This resulted in detection levels between.5% and 3% of the sensor's full range. Figure 2 is the SIMULINK diagram of the HFIR Calibration Monitoring System. 4. Drift Error Detection A simulated drift of the reactor inlet temperature (sensor #) was inserted at a rate of.5% per day. The drift was detected in about 9 hours which corresponds to a.2% detection threshold. Figure 4 shows the results of this test. The upper graph shows the simulated faulty signal and the estimate of the signal. The faulty sensor signal is declining while the estimate is steady. Also note that the estimate has much less noise than the measured signal. This is because the other 2 signals are primarily being used to estimate the signal and their noises tend to cancel. The middle graph shows the residual while the lower graph is the output of the SPRT based decision module. An SPRT output of zero corresponds to a fault free sensor and an output of one means the sensor is faulty. The output is reset to zero after a decision is made that the sensor is faulty. When the residual is so large that the decision can be made in one sample interval, the output locks high. This example shows that the based system can detect and identify very small sensor drifts. 4.2 Network Tuning Next, the system that was only trained with the first 2 days of sensor data was tested using data late in the fuel cycle. Since the network has never been trained on data from this operating condition, we would expect poor performance which Figure 5 shows. The system was tuned to the new operating condition by adjusting only the output layer weights. To do this, a new training set was made by appending patterns from the first hour of new operation to the old training set and using the SVD methodology discussed in Section 2.4.. Figure 6 shows the network performance after retuning the network. The test data used for this validation was collected from a period sampled subsequent to the retuning data. This simple and fast retuning resulted in the system being able to correctly estimate the sensor outputs. Drift detection tests gave results similar to those initially discussed in Section 4.. 5. Conclusions The results of this study have shown that a plant wide sensor calibration monitoring system using autoassociative neural networks is not only feasible but practical. The system is composed of an sensor estimation module and a SPRT based fault detection module. A faulty sensor replacement module and a model tuning module have also been implemented and tested. The complete sensor monitoring system has been integrated using The MathWork's SIMULINK software and applied to both the Crystal River Nuclear Power Plant and the High Flux Isotope Reactor. The results show that sensor degradation can be detected at levels between.2% and 3% of their full range. Not only is the fault detected, but the sensor signal could be replaced with a fault free signal so that plant operations could continue. Lastly, the output layer tuning method has proven to be an efficient means to correct for changes in plant operating conditions. 6. Acknowledgments This project was partially sponsored by the DOE through Sandia National Laboratories under contract document AQ-6982. We would also like to thank Florida Power Corporation and Oak Ridge National Laboratory for supplying the data for the project. 7. References [] B. R. Upadhyaya and E. Eryurek, "Application of Neural Networks for Validation and Plant Monitoring," Nuclear Technology, vol. 97, pp. 7-76, February, 992. [2] D. J. Wrest, J. W. Hines, and R. E. Uhrig, "Instrument Surveillance and Calibration Verification Through Plant Wide Monitoring Using Autoassociative Neural Networks", published in the proceedings of The 996 American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, University Park, PA, 996. [3] D. J. Wrest, J. W. Hines, and R. E. Uhrig, "Instrument Surveillance and Calibration Verification to Improve Nuclear Power Plant Reliability and Safety Using Autoassociative Neural Networks", published in the proceedings of The International Atomic Energy Specialist Meeting on Monitoring and Diagnosis Systems to Improve Nuclear Power Plant Reliability and Safety, Barnwood, Glouster, United Kingdom, May 4-7, 996. [4] J. W. Hines, D. J. Wrest, and R. E. Uhrig, "Plant Wide Calibration Monitoring", published in the

proceedings of The 996 IEEE International Symposium on Intelligent Control, Sept. 5-8, pp. 378-383, 996. [5] K. Nabeshima, K. Susuki and T. Turkan, "Real-Time Nuclear Power Plant Monitoring With Hybrid Artificial Intelligence Systems," published in the proceedings of The 9th Power Plant Dynamics, Control & Testing Symposium, vol. 2, pp. 5., University of Tennessee- Knoxville, May 24-26, 995. [6] M.A. Kramer, "Nonlinear Principal Component Analysis Using Autoassociative Neural Networks," AIChE Journal, vol. 37, no. 2, pp. 233-243, February, 99. [7] M.A. Kramer, "Autoassociative Neural Networks", Computers in Chemical Engineering, vol. 6, no. 4, pp. 33-328, 992. [8] D. Dong and T.J. McAvoy, " Data Analysis Using Autoassociative Neural Nets," Proceedings Of World Congress On Neural Networks, vol., pp. 6-66, San Diego, June 5-9, 994. [9] Wald, A., "Sequential Tests of Statistical Hypothesis", Ann. Math. Statist., vol. 6, pp. 7-86, 945. [] Upadhyaya, B. R., Wolvaardt, F. P., Glockler, O., "An Integrated Approach for Failure Detection in Dynamic Systems", Research Report prepared for the Measurement & Control Engineering Center, Report No. NE-MCEC-BRU-87-, 987. [] Mills, P., A. Y. Zomaya, and M. Tade, Neuro- Adaptive Process Control: A Practical Approach, John Wiley and Sons, Chichester, England, 996. [2] Lo, James Ting-Ho, "Adptive System Identification by Non-Adaptively Trained Neural Networks", proceedings of The 996 International Conference on Neural Networks, Washington DC, pp. 266-27, June 3-6, 996, [3] Masters, T., Practical Neural Network Recipes in C++, pp. 8-85, Academic Press, San Diego, 993. [4] Uhrig, R. E., J. W. Hines, C. Black, D. Wrest, and X. Xu, "Instrument Surveillance and Calibration Verification System", Sandia National Laboratory contract AQ-6982 Final Report by The University of Tennessee, March, 996. [5] SIMULINK Dynamic System Simulation Software, The Math Works, Natick, MA, 993. [6] NeuralWorks Predict, Neural Ware, Inc., Pittsburgh, PA, 995. Reset Reset 2 Reset 3 Reset 4 Reset 5 Reset 6 hfir_3.mat Data Correction ModuleStatus 3 - + Sum Estimates Residuals Mux Mux yout To Workspace Reset 7 Reset 8 Reset 9 Reset all Actual 3 SPRTs 3 Filters 3 Fault Hypothesis Reset Reset Reset 2 Load HFIR Data Reset 3 Figure 2. HFIR Calibration Monitoring System

Index Index MLB/HR MLB/HR 76 R234: Reactor Loop Flow - A 75 74 73 72 2 5 5 2 25 Difference Between Signal And Estimate - -2 5 5 2 25 SPRT Fault Hypothesis.5 5 5 2 25 time in 2 minute intervals (7/6/93 to 7/2/93) Figure 3. A Drift High of.% Per Day in FPC R234. Comp Pt. 34: #3 Reactor Inlet Temperature (F) (adapted) 22 2 2 9 8 2 3 4 5 6 7 8 9 Difference Between Signal And Estimate 5-5 2 3 4 5 6 7 8 9 SPRT Fault Hypothesis.5 2 3 4 5 6 7 8 9 time in 2 minute intervals Figure4..5% Per Day Simulated Drift in HFIR #

Index Index 25 Comp Pt. 34: #3 Reactor Inlet Temperature (F) 2 5 5 2 3 4 5 6 7 8 9 Difference Between Signal And Estimate -5 2 3 4 5 6 7 8 9 SPRT Fault Hypothesis.5 2 3 4 5 6 7 8 9 time in 2 minute intervals Figure 5. Testing of HFIR Monitoring System With Data From a New Operating Condition 22 Comp Pt. 34: #3 Reactor Inlet Temperature (F) 2 2 9 8 5 2 3 4 5 6 7 8 9 Difference Between Signal And Estimate -5 2 3 4 5 6 7 8 9 SPRT Fault Hypothesis.5 2 3 4 5 6 7 8 9 time in 2 minute intervals Figure 6. Testing of HFIR Monitoring System After Tuning