Use of Autoassociative Neural Networks for Signal Validation
|
|
- Maurice Anderson
- 5 years ago
- Views:
Transcription
1 "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 Darryl J. Wrest Systems Health Management Group Honeywell Technology Center Minneapolis, Minnesota 5548 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
2 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.
3 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.
4 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 R R R % 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
5 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 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 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 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
6 proceedings of The 996 IEEE International Symposium on Intelligent Control, Sept. 5-8, pp , 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 , February, 99. [7] M.A. Kramer, "Autoassociative Neural Networks", Computers in Chemical Engineering, vol. 6, no. 4, pp , 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 , 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 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
7 Index Index MLB/HR MLB/HR 76 R234: Reactor Loop Flow - A Difference Between Signal And Estimate SPRT Fault Hypothesis 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) Difference Between Signal And Estimate SPRT Fault Hypothesis time in 2 minute intervals Figure4..5% Per Day Simulated Drift in HFIR #
8 Index Index 25 Comp Pt. 34: #3 Reactor Inlet Temperature (F) Difference Between Signal And Estimate SPRT Fault Hypothesis 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) Difference Between Signal And Estimate SPRT Fault Hypothesis time in 2 minute intervals Figure 6. Testing of HFIR Monitoring System After Tuning
ON-LINE SENSOR CALIBRATION MONITORING AND FAULT DETECTION FOR CHEMICAL PROCESSES
ON-LINE SENSOR CALIBRATION MONITORING AND FAULT DETECTION FOR CHEMICAL PROCESSES Xiao Xu, J. Wesley Hines, Robert E. Uhrig Maintenance and Reliability Center The University of Tennessee Knoxville, TN 37996-23
More informationImplementing a Reliable Leak Detection System on a Crude Oil Pipeline
Implementing a Reliable Leak Detection System on a Crude Oil Pipeline By Dr Jun Zhang & Dr Enea Di Mauro* 1. Introduction Pipeline leak detection or integrity monitoring (PIM) systems have been applied
More informationReal Time Pipeline Leak Detection on Shell s North Western Ethylene Pipeline
Real Time Pipeline Leak Detection on Shell s North Western Ethylene Pipeline Dr Jun Zhang & Dr Ling Xu* REL Instrumentation Limited ABSTRACT In the past ten years, a number of pipeline leak detection systems
More informationSYNERGY IN LEAK DETECTION: COMBINING LEAK DETECTION TECHNOLOGIES THAT USE DIFFERENT PHYSICAL PRINCIPLES
Proceedings of the 2014 10 th International Pipeline Conference IPC2014 September 29-October 3, 2014, Calgary, Alberta, Canada IPC2014-33387 SYNERGY IN LEAK DETECTION: COMBINING LEAK DETECTION TECHNOLOGIES
More informationRLDS - Remote LEAK DETECTION SYSTEM
RLDS - Remote LEAK DETECTION SYSTEM Asel-Tech has spent considerable time and resources over the past 8 years to improve our technology, to the point where it is unparalleled in reliability and performance
More informationPipeline Leak Detection: The Esso Experience
Pipeline Leak Detection: The Esso Experience Bruce Tindell, Project Manager, Esso Petroleum Company Ltd, UK Dr Jun Zhang, Managing Director, ATMOS International (formerly REL Instrumentation) Abstract
More informationFire Risks of Loviisa NPP During Shutdown States
Fire Risks of Loviisa NPP During Shutdown States Sami Sirén a*, Ilkka Paavola a, Kalle Jänkälä a a Fortum Power And Heat Oy, Espoo, Finland Abstract: Fire PRA for all 15 shutdown states of Loviisa NPP
More informationReduced Order WECC Modeling for Frequency Response and Energy Storage Integration
Reduced Order WECC Modeling for Frequency Response and Energy Storage Integration Pranathi Bhattacharji, Student Member, IEEE, Ted K. A. Brekken, Senior Member, IEEE School of Electrical Engineering and
More informationJET ENGINE SENSOR VALIDATION WITH PROBABILISTIC NEURAL NETWORKS
JET ENGINE SENSOR VALIDATION WITH PROBABILISTIC NEURAL NETWORKS C Romessis Research Assistant K Mathioudakis Associate Professor Laboratory of Thermal Turbomachines National Technical University of Athens
More informationFailure Modes, Effects and Diagnostic Analysis
Failure Modes, Effects and Diagnostic Analysis Project: Detcon FP-700 Combustible Gas Sensor Customer: Detcon The Woodlands, TX USA Contract No.: DC 06/08-04 Report No.: DC 06/08-04 R001 Version V1, Revision
More informationApplication Note. Application Note for BAYEX
Application Note Application Note for BAYEX Preface This application note provides the user a more detailed description of the Bayesian statistical methodology available in Version 8.05 and above, of the
More informationPerformance Neuro-Fuzzy for Power System Fault Location
International Journal of Engineering and Technology Volume 3 No. 4, April, 2013 Performance Neuro-Fuzzy for Power System Fault Location 1,2 Azriyenni, 1 M.W. Mustafa 1 Electrical Engineering, Fakulti Kejuruteraan
More informationConceptual Design of a Better Heat Pump Compressor for Northern Climates
Purdue University Purdue e-pubs International Compressor Engineering Conference School of Mechanical Engineering 1976 Conceptual Design of a Better Heat Pump Compressor for Northern Climates D. Squarer
More informationUSER APPROVAL OF SAFETY INSTRUMENTED SYSTEM DEVICES
USER APPROVAL OF SAFETY INSTRUMENTED SYSTEM DEVICES Angela E. Summers, Ph.D., P.E, President Susan Wiley, Senior Consultant SIS-TECH Solutions, LP Process Plant Safety Symposium, 2006 Spring National Meeting,
More informationDynamic Simulation of Double Pipe Heat Exchanger using MATLAB simulink
Dynamic Simulation of Double Pipe Heat Exchanger using MATLAB simulink 1 Asoka R.G, 2 Aishwarya N, 3 Rajasekar S and 4 Meyyappan N 1234 Department of Chemical Engineering Sri Venkateswara College of Engineering,
More informationAdvanced Pattern Recognition for Anomaly Detection Chance Kleineke/Michael Santucci Engineering Consultants Group Inc.
Advanced Software Technologies Advanced Pattern Recognition for Anomaly Detection Chance Kleineke/Michael Santucci Engineering Consultants Group Inc. August 16, 2017 Tampa Convention Center Tampa, Florida
More informationResults of Recent DOE Research on Development of Cable Condition Monitoring and Aging Management Technologies
Results of Recent DOE Research on Development of Cable Condition Monitoring and Aging Management Technologies C.J. Campbell, J.B. McConkey, H.M. Hashemian, C.D. Sexton, D.S. Cummins Analysis and Measurement
More informationMeasure Lifetime Derived from a Field Study of Age at Replacement
Measure Lifetime Derived from a Field Study of Age at Replacement David Robison, MicroGrid David Cohan Bruce True, Portland General Electric The traditional engineering technique for estimating the expected
More informationNEW CD WARP CONTROL SYSTEM FOR THE CORRUGATING INDUSTRY
NEW CD WARP CONTROL SYSTEM FOR THE CORRUGATING INDUSTRY USING A NEW CONCEPT IN MOISTURE SENSING AND CONTROL BY DRYING TECHNOLOGY, INC A New CD Warp Control System For the Corrugating Industry Introduction:
More informationUsing Neural Networks for Alarm Correlation in Cellular Phone Networks
Using Neural Networks for Alarm Correlation in Cellular Phone Networks Hermann Wietgrefe, Klaus-Dieter Tuchs, Klaus Jobmann, Guido Carls, Peter Fröhlich*, Wolfgang Nejdl*, Sebastian Steinfeld* Institut
More informationSTUDY ON THE CONTROL ALGORITHM OF THE HEAT PUMP SYSTEM FOR LOAD CHANGE
Numbers of Abstract/Session (given by NOC) - 1 - STUDY ON THE CONTROL ALGORITHM OF THE HEAT PUMP SYSTEM FOR LOAD CHANGE Seok Ho Yoon, Kong Hoon Lee, Chan Ho Song, and Ook Joong Kim Environment and Energy
More informationRESULTS FROM HOUSE APPLIANCE SAFETY AND DEPRESSURIZATION TESTS CONDUCTED ON SINGLE FAMILY HOUSES UNDERGOING SOUND INSULATION
RESULTS FROM HOUSE APPLIANCE SAFETY AND DEPRESSURIZATION TESTS CONDUCTED ON SINGLE FAMILY HOUSES UNDERGOING SOUND INSULATION DL Bohac * Center for Energy and Environment, Minneapolis, MN USA ABSTRACT Extensive
More informationNumerical Standards Listing
ISA-RP2.1-1978 - Manometer Tables Numerical Standards Listing ISA-5.1-1984 (R1992) - Instrumentation Symbols and Identification (Formerly ANSI/ISA-5.1-1984 [R1992]) ISA-5.2-1976 (R1992) - Binary Logic
More informationLink loss measurement uncertainties: OTDR vs. light source power meter By EXFO s Systems Engineering and Research Team
Link loss measurement uncertainties: OTDR vs. light source power meter By EXFO s Systems Engineering and Research Team INTRODUCTION The OTDR is a very efficient tool for characterizing the elements on
More informationOVERVIEW AND FUTURE DEVELOPMENT OF THE NEUTRON SENSOR SIGNAL SELF-VALIDATION (NSV) PROJECT
OVERVIEW AND FUTURE DEVELOPMENT OF THE NEUTRON SENSOR SIGNAL SELF-VALIDATION (NSV) PROJECT Jean-Christophe Trama, Alain Bourgerette, Eric Barat, Bernard Lescop LETI (CEA - Advanced Technologies) CEA/Saclay
More informationNumerical Standards Listing
ISA-RP2.1-1978 - Manometer Tables Numerical Standards Listing ANSI/ISA-5.1-1984 (R1992) - Instrumentation Symbols and Identification ANSI/ISA-5.2-1976 (R1992) - Binary Logic Diagrams for Process Operations
More informationHow Computer Simulation Helps to Design Induction Heating Systems
Large Pipe Powder Coating Page 1 ASM 2001 How Computer Simulation Helps to Design Induction Heating Systems Dr. Valentin S. Nemkov, Chief Scientist Mr. Robert C. Goldstein, Research Engineer Mr. Robert
More informationWorkshop on AFDD for RTUs Moving from R&D to Commercialization July 13, Introduction
Workshop on AFDD for RTUs Moving from R&D to Commercialization July 13, 2014 Introduction Jim Braun Ray W. Herrick Laboratories Purdue University West Lafayette, IN 47907 Slide 1 Acknowledgement This workshop
More information6 th Pipeline Technology Conference 2011
6 th Pipeline Technology Conference 2011 Pipeline Leak Detection and Theft Detection Using Rarefaction Waves Authors: Dr Alex Souza de Joode, VP International Operations; ATMOS International, UK. Andrew
More informationHome appliances simulator for smart home systems testing
Home appliances simulator for smart home systems testing PETER JANIGA, MARTIN LIŠKA, ANTON BELÁŇ, VLADIMÍR VOLČKO, MARIAN IVANIČ Department of Electrical Power Engineering Slovak University of Technology
More informationSome Modeling Improvements for Unitary Air Conditioners and Heat Pumps at Off-Design Conditions
Purdue University Purdue e-pubs International Refrigeration and Air Conditioning Conference School of Mechanical Engineering 2006 Some Modeling Improvements for Unitary Air Conditioners and Heat Pumps
More informationAVOID CATASTROPHIC SITUATIONS: EXPERT FIRE AND GAS CONSULTANCY OPTIMIZES SAFETY
AVOID CATASTROPHIC SITUATIONS: EXPERT FIRE AND GAS CONSULTANCY OPTIMIZES SAFETY World-class services help reduce incidents, protect the environment, and keep people and plants safe White Paper PAGE 1 Introduction
More informationFuzzy Logic Based Coolant Leak Detection
Volume 118 No. 5 2018, 825-832 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Fuzzy Logic Based Coolant Leak Detection 1 J.Suganthi, M.E., 2 G. Nithya,
More informationClemens Felsmann 1, Jean Lebrun 2, Vincent Lemort 2 and Aad Wijsman 3 ABSTRACT INTRODUCTION METHODOLOGY
Eleventh International IBPSA Conference Glasgow, Scotland July 27-30, 2009 TESTING AND VALIDATION OF SIMULATION TOOLS OF HVAC MECHANICAL EQUIPMENT INCLUDING THEIR CONTROL STRATEGIES. PART II: VALIDATION
More informationAVOID CATASTROPHIC SITUATIONS: EXPERT FIRE AND GAS CONSULTANCY OPTIMIZES SAFETY
AVOID CATASTROPHIC SITUATIONS: EXPERT FIRE AND GAS CONSULTANCY OPTIMIZES SAFETY World-class services help reduce incidents, protect the environment, and keep people and plants safe White Paper PAGE 1 Introduction
More informationICS Regent. Fire Detector Input Modules PD-6032 (T3419)
ICS Regent Fire Detector Input Modules (T3419) Issue 1, March, 06 Fire detector input modules provide interfaces for 16 fire detector inputs such as smoke detectors, flame detectors, temperature detectors,
More informationComputer Modelling and Simulation of a Smart Water Heater
Computer Modelling and Simulation of a Smart Water Heater Maria Kathleen Ellul University of Malta ellul_maria@yahoo.com Adrian Muscat University of Malta adrian.muscat@um.edu.mt Abstract A computational
More informationThe Use of Fuzzy Spaces in Signal Detection
The Use of Fuzzy Spaces in Signal Detection S. W. Leung and James W. Minett Department of Electronic Engineering, City University of Hong Kong Correspondence to: Dr. Peter S. W. Leung Department of Electronic
More informationA Simulation Study on the Energy Efficiency of Gas-Burned Boilers in Heating Systems
International Journal of Energy and Power Engineering 2015; 4(6): 327-332 Published online December 5, 2015 (http://www.sciencepublishinggroup.com/j/ijepe) doi: 10.11648/j.ijepe.20150406.11 ISSN: 2326-957X
More informationSimple Equations for Predicting Smoke Filling Time in Fire Rooms with Irregular Ceilings
Fire Science and Technorogy Vol.24 No.4(2005) 165-178 165 Simple Equations for Predicting Smoke Filling Time in Fire Rooms with Irregular Ceilings Jun-ichi Yamaguchi 1, Takeyoshi Tanaka 2 1 Technical Research
More informationAPI MANUAL OF PETROLEUM MEASUREMENT STANDARDS
API MANUAL OF PETROLEUM MEASUREMENT STANDARDS Chapter 22 Testing Protocols Section 1 General Guidelines for Developing Testing Protocols for Devices Used in the Measurement of Hydrocarbon Fluids Type Testing
More informationNumerical study of heat pipe application in heat recovery systems
Numerical study of heat pipe application in heat recovery systems *Song Lin, John Broadbent, Ryan McGlen Thermacore Europe, 12 Wansbeck Business Park Ashington, Northumberland NE3 QW, UK E-mail: song.lin@thermacore.com
More informationZONE MODEL VERIFICATION BY ELECTRIC HEATER
, Volume 6, Number 4, p.284-290, 2004 ZONE MODEL VERIFICATION BY ELECTRIC HEATER Y.T. Chan Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China ABSTRACT Selecting
More informationTESTS OF ADSIL COATING
TESTS OF ADSIL COATING Test B - Long Term Test FSEC-CR-1259-01 July 11, 2001 Prepared for: Bob Suggs Florida Power & Light Company 9250 W. Flagler Street Miami, Florida 33174 Principal Investigator Dr.
More informationThis paper will discuss the leak detection technologies that have been adapted to detect thefts and secure accurate tapping point locations.
Presenter: David Dingley Organization: Atmos International Country: United Kingdom Abstract Pipeline theft is a serious global problem and has been on the rise for the last few years. Petroleum thefts
More informationFlexibility, scalability andsecurity
THE OF INFORMATION TECHNOLOGY SYSTEMS An Official Publication of BICSI January/February 2014 l Volume 35, Number 1 data center Flexibility, scalability andsecurity plus + The Next Five Years in AV + Measuring
More informationCompression of Fins pipe and simple Heat pipe Using CFD
Compression of Fins pipe and simple Heat pipe Using CFD 1. Prof.Bhoodev Mudgal 2. Prof. Gaurav Bhadoriya (e-mail-devmudgal.mudgal@gmail.com) ABSTRACT The aim of this paper is to identify the advantages
More informationa high level of operational reliability), and the designer (in performing probabilistic-based
By Edward K. Budnick, P.E. INTRODUCTION When automatic fire sprinkler systems, or any fire protection safety features, are included in a fire protection design package, it is assumed that, if needed, they
More informationSmartLine Pressure Transmitters Modular, Accurate and Robust for the Lowest Cost of Ownership
Field Products SmartLine Pressure Transmitters Modular, Accurate and Robust for the Lowest Cost of Ownership Honeywell Innovation and Expertise When faced with demanding fi eld instrument application requirements,
More informationCertification Report of the ST3000 Pressure Transmitter
Certification Report of the ST3000 Pressure Transmitter Revision No.: 1.0 Date: Report Number: Product: Customer: Order Number: Authority: Responsible: 2006-Dec-12 SAS-128/2006T ST3000 Pressure Transmitter
More informationTemperature Control of Heat Exchanger Using Fuzzy Logic Controller
Vo1ume 1, No. 04, December 2014 999 Temperature Control of Heat Exchanger Using Fuzzy Logic Controller Aravind R. Varma and Dr.V.O. Rejini Abstract--- Fuzzy logic controllers are useful in chemical processes
More informationHow K-Patents DD-23 System is Built to Conform to BLRBAC Recommendations
PAGE(S) 1 (15) How K-Patents DD-23 System is Built to Conform to BLRBAC Recommendations This document describes how K-Patents Digital Divert Control System DD-23 is built strictly according to the BLRBAC
More informationProceedings Design, Fabrication and Optimization of a Silicon MEMS Natural Gas Sensor
Proceedings Design, Fabrication and Optimization of a Silicon MEMS Natural Gas Sensor Marjan Shaker 1,, Erik Sundfør 3, Gaël Farine 3, Conor Slater 3, Pierre-André Farine 1 and Danick Briand, * 1 Electronic
More informationCertificate: / 29 April 2014
Test report: 936/21217455/A of 10 September 2013 Initial certification: 01 April 2014 Expiry date: 31 March 2019 Publication: BAnz AT 01 April 2014 B12, chapter I, No. 1.1 Approved application The tested
More informationImproving Heating Performance of a MPS Heat Pump System With Consideration of Compressor Heating Effects in Heat Exchanger Design
Purdue University Purdue e-pubs International Refrigeration and Air Conditioning Conference School of Mechanical Engineering 2006 Improving Heating Performance of a MPS Heat Pump System With Consideration
More informationModeling of Ceiling Fan Based on Velocity Measurement for CFD Simulation of Airflow in Large Room
Modeling of Ceiling Fan Based on Velocity Measurement for CFD Simulation of Airflow in Large Room Y. Momoi 1, K. Sagara 1, T. Yamanaka 1 and H. Kotani 1 1 Osaka University, Graduate School of Eng., Dept.
More informationGMA 301. Operation Manual. Worldwide Supplier of Safety Solutions. Part Number
Worldwide Supplier of Safety Solutions GfG Instrumentation 1194 Oak Valley Drive #20 Phone: 734-769-0573 Fax: 734-769-1888 E-Mail: info@gfg-inc.com Internet: www.gfg-inc.com GMA 301 Operation Manual Part
More informationSIL DETERMINATION AND PROBLEMS WITH THE APPLICATION OF LOPA
SIL DETERMINATION AND PROBLEMS WITH THE APPLICATION OF LOPA Alan G King Hazard & Reliability Specialist, ABB Engineering Services, Billingham, Cleveland UK. TS23 4YS For a number of years, industry has
More informationSafety. Reliability. Experience.
Safety. Reliability. Experience. Instrumentation and Control Systems Class 1E Safety Balance of Plant Sensors and Transmitters System Upgrade Solutions Obsolescence Support Nuclear Quality Assurance (NQA)
More informationEvaluation of the Incon TS-LLD Line Leak Detection System
Evaluation of the Incon TS-LLD Line Leak Detection System (for Hourly Testing, Monthly Monitoring, and Annual Line Tightness Testing) EPA Forms PREPARED FOR Incon (Intelligent Controls) July 6, 1995 Ken
More informationDiagnostics with fieldbus
2002 Emerson Process Management. All rights reserved. View this and other courses online at www.plantwebuniversity.com. Fieldbus 105 Diagnostics with fieldbus Overview More than device maintenance Equipment
More informationGAS DETECTOR LOCATION. Ø.Strøm and J.R. Bakke, GexCon AS, Norway
AUTHOR BIOGRAPHICAL NOTES GAS DETECTOR LOCATION Ø.Strøm and J.R. Bakke, GexCon AS, Norway Øyvind Strøm graduated in 1996 from Stavanger University College with a M.Sc. degree in Offshore Safety Technology.
More informationOrdered Fuzzy ARTMAP: A Fuzzy ARTMAP algorithm with a fixed order
Ordered Fuzzy ARTMAP: A Fuzzy ARTMAP algorithm with a fixed order of pattern present at ion I. Dagher*, M. Georgiopoulos', G. L. Heileman**, G. Bebis*** * Department of Electrical and Computer Engineering
More informationR&D for the improvement of O&M in CSP plants. Dr. Marcelino Sánchez. - November,
R&D for the improvement of O&M in CSP plants. Dr. Marcelino Sánchez - November, 2015 - í n d i c e 1 Need of R&D for O&M improvement in CSP Plants 2 Current R&D activities in O&M improvement carried out
More informationSafety Instrumented Systems
Safety Instrumented Systems What is a Safety Instrumented System? A Safety Instrumented System SIS is a new term used in standards like IEC 61511 or IEC 61508 for what used to be called Emergency Shutdown
More informationHow to Use Fire Risk Assessment Tools to Evaluate Performance Based Designs
How to Use Fire Risk Assessment Tools to Evaluate Performance Based Designs 1 ABSTRACT Noureddine Benichou and Ahmed H. Kashef * Institute for Research in Construction National Research Council of Canada
More informationHeat Transfer in Evacuated Tubular Solar Collectors
Heat Transfer in Evacuated Tubular Solar Collectors Graham L. Morrison, Indra Budihardjo and Masud Behnia School of Mechanical and Manufacturing Engineering University of New South Wales Sydney 2052 Australia
More informationA FIRST RESPONDERS GUIDE TO PURCHASING RADIATION PAGERS
EML-624 A FIRST RESPONDERS GUIDE TO PURCHASING RADIATION PAGERS FOR HOMELAND SECURITY PURPOSES Paul Bailey Environmental Measurements Laboratory U.S. Department of Homeland Security 201 Varick Street,
More informationFREQUENCY ENHANCEMENT OF DUAL-JUNCTION THERMOCOUPLE PROBES
XXIII Biannual Symposium on Measuring Techniques in Turbomachinery Transonic and Supersonic Flow in FREQUENCY ENHANCEMENT OF DUAL-JUNCTION THERMOCOUPLE PROBES James Braun Purdue University Indiana, United
More informationField Products. Experion LX. Proven DCS for a wide range of industrial applications
Field Products Experion LX Proven DCS for a wide range of industrial applications Tried-and-True Technology. Experion LX is an extension of Honeywell s award-winning Experion Process Knowledge System (PKS)
More informationCFD Analysis of temperature dissipation from a hollow metallic pipe through circular fins using Ansys 14.5
IJAET International Journal of Application of Engineering and Technology ISSN: 2395-3594 Vol-1 No.-2 CFD Analysis of temperature dissipation from a hollow metallic pipe through circular fins using Ansys
More informationResearch on Decision Tree Application in Data of Fire Alarm Receipt and Disposal
Research Journal of Applied Sciences, Engineering and Technology 5(22): 5217-5222, 2013 ISSN: 2040-7459; e-issn: 2040-7467 Maxwell Scientific Organization, 2013 Submitted: October 09, 2012 Accepted: December
More informationPresented at 6 th Pipeline Technology Conference Hannover, Germany April 4-5, 2011
Presented at 6 th Pipeline Technology Conference 2011 Hannover, Germany April 4-5, 2011 Introduction Recent Pipeline Leak Detection History in the US Inspection Finding and Enforcement Actions Overview
More informationExperimental Study to Evaluate Smoke Stratification and Layer Height in Highly Ventilated Compartments
Experimental Study to Evaluate Smoke Stratification and Layer Height in Highly Ventilated Compartments Jason Huczek a, Marc Janssens a, Kentaro Onaka b, Stephen Turner c a SwRI, 6220 Culebra Road, San
More informationDTW Master Specification Section EMCS: Start Up, Verification and Commissioning
Page 1 of 6 PART 1 GENERAL 1.1 RELATED SECTIONS.1 Related Sections..1 The contractor is to ensure that all related work is co ordinated among all specification sections, as well as between Division 11,
More informationBenefits of Enhanced Event Analysis in. Mark Miller
Benefits of Enhanced Event Analysis in Data Center OTDR Testing Mark Miller Dr. Fang Xu AFL/Noyes Test & Inspection Overview Challenges Topics Techniques and Improvements Benefits of enhanced event analysis
More informationIntrusion Detection System: Facts, Challenges and Futures. By Gina Tjhai 13 th March 2007 Network Research Group
Intrusion Detection System: Facts, Challenges and Futures By Gina Tjhai 13 th March 2007 Network Research Group 1 Overview Introduction Challenges of current IDS Potential solutions Alarm Correlation Existing
More informationEffective Biomass Moisture Control
Effective Biomass Moisture Control By John Robinson Drying Technology, Inc P.O. Box 1535 Silsbee, TX 77656 409-385-6422/6537fax john@moistureconrols.com www.moisturecontrols.com Presented at The Timber
More informationMODELING OF THE SINGLE COIL, TWIN FAN AIR-CONDITIONING SYSTEM IN ENERGYPLUS
MODELING OF THE SINGLE COIL, TWIN FAN AIR-CONDITIONING SYSTEM IN ENERGYPLUS Clayton Miller 1,* and Chandra Sekhar 1 1 National University of Singapore, Singapore * Corresponding email: miller.clayton@nus.edu.sg
More informationSpeed and Frequency Seite 1 von 7
Speed and Frequency Seite 1 von 7 E16 Systems for High Safety Speed ing, all with Triple Modular Redundancy. A choice of versions to meet various demands. Compliant with SIL3 / IEC 61508 and/or API 670.
More informationCHOOSING A FIRE VENTILATION STRATEGY FOR AN UNDERGROUND METRO STATION
- 165 - CHOOSING A FIRE VENTILATION STRATEGY FOR AN UNDERGROUND METRO STATION Wojciech Węgrzyński, Grzegorz Krajewski, Paweł Sulik Fire Research Department, Building Research Institute (ITB), Poland ABSTRACT
More informationASHRAE JOURNAL ON REHEAT
Page: 1 of 7 ASHRAE JOURNAL ON REHEAT Dan Int-Hout Chief Engineer Page: 2 of 7 Overhead Heating: A lost art. March 2007 ASHRAE Journal Article Dan Int-Hout Chief Engineer, Krueger VAV terminals provide
More informationNumerical Standards Listing
Numerical Standards Listing ISA-RP2.1-1978 - Manometer Tables ISA-5.1-1984 (R1992) - Instrumentation Symbols and Identification (Formerly ANSI/ISA-5.1-1984 [R1992]) ISA-5.2-1976 (R1992) - Binary Logic
More informationAssessment of the Safety Integrity of Electrical Protection Systems in the Petrochemical Industry
Assessment of the Safety Integrity of Electrical Protection Systems in the Petrochemical Industry 1. Introduction Author: Colin Easton ProSalus Limited ~ Independent Safety Consultants Within the United
More informationSafety Transmitter / Logic Solver Hybrids. Standards Certification Education & Training Publishing Conferences & Exhibits
Safety Transmitter / Logic Solver Hybrids Standards Certification Education & Training Publishing Conferences & Exhibits Traditional Pressure Sensor Portfolio Trip Alarm or Trip Module Process Transmitter
More informationAdvanced HART Diagnostic Suite
Reference Manual Section 7 Rosemount 3051S Series Advanced HART Diagnostic Suite Overview....................................... page 7-1 User Interface................................... page 7-3 Statistical
More informationFlameGard 5 MSIR HART
FlameGard 5 MSIR HART Multi-Spectral Infrared Flame Detector HART Communication with the FlameGard 5 Multi-spectral Infrared Detector The information and technical data disclosed in this document may be
More informationOVEN INDUSTRIES, INC.
OVEN INDUSTRIES, INC. OPERATING MANUAL Model 5C7-252 TEMPERATURE CONTROLLER With PLC Inputs Introduction Thank you for purchasing our controller. The Model 5C7-252 is an exceptionally versatile unit and
More informationProcess Safety - Market Requirements. V.P.Raman Mott MacDonald Pvt. Ltd.
Process Safety - Market Requirements V.P.Raman Mott MacDonald Pvt. Ltd. Objective of Process Safety Protect personnel Protect the environment Protect the plant equipment / production. Multiple Layers
More informationp?:uam SYSTEMS Technical Progress Report Jane H. Davidson NAllJRAL CONVECTION HEAT EXCHANGERS FOR SOLAR WATER HEATING February 1,1996 to March 31,1996
NAllJRAL CONVECTION HEAT EXCHANGERS FOR SOLAR WATER HEATING SYSTEMS Technical Progress Report February 1,1996 to March 31,1996 Jane H Davidson Department of Mechanical Engineering University of Minnesota
More informationVideo Smoke Detection using Deep Domain Adaptation Enhanced with Synthetic Smoke Images
Video Smoke Detection using Deep Domain Adaptation Enhanced with Synthetic Smoke Images Gao Xu, Qixing Zhang, Gaohua Lin, Jinjun Wang, Yongming Zhang State Key Laboratory of Fire Science, University of
More information1 Introduction - The rating level. 3 Subjective or objective analysis? 2 Determination of the adjustment for annoying tones
Noise can be said to be tonal if it contains a distinguishable, discrete, continuous note. This may include a whine, hiss, screech, hum, etc., and any such subjective finding is open to discussion when
More informationSoftware Version 2.01 LEVEL MONITOR MODEL 220
Software Version 2.01 LEVEL MONITOR MODEL 220 19 April 2000 CONTENTS 1. Introduction 1 1.1 Model Number Designation 2 1.2 Intrinsic Safety Considerations 3 2. Specification 4 3. Operation 6 3.1 Display
More informationSafety in the process industry
Products Solutions Services Safety in the process industry Simply reliable Table of contents Endress+Hauser: At home in the process safety Smart devices and concepts for hazardous areas Introduction to
More informationReducing the Carbon Footprint of Existing Domestic Heating: A Non-Disruptive Approach
EEDAL 2009 16-18 June 2009 Reducing the Footprint of Existing Domestic Heating: A Non-Disruptive Approach Martin O Hara Danfoss Randall Limited Abstract There is insufficient time between today and 2020
More informationFault Isolation for Spacecraft Systems: An Application to a Power Distribution Testbed
Preprints of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS) Fault Isolation for Spacecraft Systems: An Application to a Power Distribution Testbed
More informationModel 1062 Environment Monitor
Model 1062 Environment Monitor Continuously Monitors Environment Temperature Humidity Barometric Pressure Line Voltage Line Frequency Applications Manufacturing The 1062A Environment Monitor will ensure
More informationUse of MCNPX for Alpha Spectrometry Simulations of a Continuous Air Monitor
Page 1 DOE/NV/XXXXX--XX Use of MCNPX for Alpha Spectrometry Simulations of a Continuous Air Monitor Robert B. Hayes, Ph.D., CHP, PE Senior Scientist Remote Sensing Laboratory PO Box 98521, Mail Stop RSL-47
More informationNew Developments in the IEC61511 Edition 2
New Developments in the IEC61511 Edition 2 Presented by Dr Issam Mukhtar PhD(Eng.) TÜV FS Expert (IDNo.:117/06) 6 th May 2013 2010 Invensys. All Rights Reserved. The names, logos, and taglines identifying
More informationUnderstanding total measurement uncertainty in power meters and detectors
Understanding total measurement uncertainty in power meters and detectors Jay Jeong, MKS Instruments. Inc. INTRODUCTION It is important that users of calibrated power meters and detectors understand and
More information