A PC-Based Signal Validation System for Nuclear Power Plants

The safe operation and efficient control of a nuclear power plant requires reliable information about the state of the process. Therefore the validity of sensors which measure the process variables is of great importance. Signal validation is the detection, isolation and characterization of faulty signals. Properly validated process signals are also beneficial from the standpoint of increased plant availability and reliability of operator actions. In recent years, several methods have been developed for signal validation (SV). Some of these methods include generalized consistency checking (GCC) , process empirical modeling (PEM) for prediction, multi-dimensional process hypercube (PHC), univariate and multivariate autoregression modeling, and expert systems. The purpose of this research is to investigate the effectiveness of a few other techniques such as artificial neural networks (ANN) and extended Kalman filters for signal estimation during steady­ state as well as transient operating conditions. The new and improved signal validation modules were integrated into one computer program for easy access. The final decision about the validity of signals was made using a fuzzy logic algorithm. The integrated system consist of the following modules: Generalized Consistency Checking (GCC), Process Empirical Modeling (PEM) Artificial Neural Network (ANN) prediction, and Kalman Filtering Technique (KFT). These modules operate in parallel and the system architecture is flexible for adding or removing a SV module. The integrated system utilizes modern graphical user interface (GUI) techniques for displaying and accessing information. Due to the popularity and the increase in computing power and the decrease in the cost of PC \u27s, nuclear power plants are also incorporating PC\u27 s into their engineering divisions to access process data over local area networks (LAN). The software in this study was therefore developed on an IBM compatible PC operating under Microsoft Windows 3.™. Hypertext buttons, compatible with different aspects of Microsoft Windows 3.1™, were provided in parts of the GUI, for displaying the processed information and the results. The dynamic form of the empirical modeling and the Kalman filtering technique showed superior performance in signal validation. The implementation details of the system were evaluated off-line, using steady-state and transient data from operating pressurized water reactor (PWR) nuclear power plants. The application of this new system was illustrated for a U-tube steam generator (UTSG) of a PWR nuclear power plant. A system executive was developed for controlling the functions of various modules, interfacing the input-output (I/O) with the environment, and for decision making. The use of new modules, improvement in the previous techniques, and the use of GUI have resulted in a robust and easily implementable signal validation system for power plants

Multi-Sensor Fusion for Induction Motor Aging Analysis and Fault Diagnosis

Induction motors are the most commonly used electrical drives, ranging in power from fractional horsepower (HP) to several thousand horsepowers. Several studies have been conducted to identify the cause of failure of induction motors in industrial applications. More than fifty percent of the failures are mechanical in nature, such as bearing, balance and alignment-related problems. Recent activities indicate a focus towards building intelligence into the motors, so that a continuous on-line fault diagnosis and prognosis may be performed. The purpose of this research and development was to perform aging studies of three-phase, squirrel-cage induction motors; establish a database of mechanical, electrical and thermal measurements from load testing of the motors; develop a sensor-fusion method for on-line motor diagnosis; and use the accelerated aging models to extrapolate to the normal aging regimes. A new laboratory was established at The University of Tennessee to meet the goals of the project. The facility consists of three motor aging modules and a motor load-testing platform. The accelerated aging and motor performance tests constitute a unique database, containing information about the trend characteristics of measured signatures as a function of motor faults. The various measurements facilitate enhanced fault diagnosis of motors and may be effectively utilized to increase the reliability of decision making and for the development of life prediction techniques. One of these signatures, which constitute the database, is the use of Multi- Resolution Analysis (MRA) using wavelets. In today\u27s industry applications, vibration signatures are analyzed only up to several hundred Hertz. The use of MRA in trending different frequency bands has revealed that higher frequencies (2-4 kiloHertz) show a characteristic increase when the condition of a bearing is in question. This study effectively showed that the use of MRA in vibration signatures can identify a thermal degradation or degradation via electrical charge of the bearing, whereas other failure mechanisms, such as winding insulation failure, do not exhibit such characteristics. A motor diagnostic system, called the Intelligent Motor Monitoring System (IMMS) was developed in this research. The IMMS integrated the various mechanical, electrical and thermal signatures, and artificial neural networks and fuzzy logic algorithms. The IMMS was then used for motor fault detection and isolation and for estimating its remaining operable lifetime. The performance of the IMMS was evaluated using the motor aging data, and showed that the stator thermal degradation, bearing thermal degradation and bearing fluting degradation could be effectively diagnosed and the prognosis of motor operation could be established. Previous studies are primarily based on the \u27cause\u27 when calculating and estimating the remaining life of a motor and its components. This work has concentrated rather on the \u27effects\u27 in the detection and isolation of faults and the remaining lifetime of the motor and its components. Hence, when \u27on-line\u27 technology implementation is in question, measuring the effects is readily available, feasible, robust and definite, rather than trying to measure the cause (e.g. measuring the cause of motor winding insulation failure by means of ambient temperature only vs. measuring the effects of motor winding insulation failure by means of winding insulation temperature, leakage voltage and zero­-component impedance)