Research Projects - Dr. B.R. UpadhyayaTitle: Automated Diagnosis of Motor-Operated Valves Using Motor Power Signature Analysis

This R&D projects focuses on the development of techniques for the condition diagnosis of motor-operated valves (MOVs). Since the mechanical parts of the valve are of primary concern, the most desirable parameter to be monitored is the mechanical load experienced by the valve operator. The electrical parameter that has the closest correlation to the MOV load is the motor power. Time-dependent characteristics of the motor power waveform are necessary to track the various events during valve stroke cycles. An expert system is being developed using the Visual C++ programming language. The expert system combines a syntactic pattern recognition module, a signal pre-processing module, a rule-based expert system for valve diagnostics, a knowledge base, an on-line help module, and an interface for Microsoft Excel for report preparation. The system is being tested using both plant and laboratory test data.

Title: Development of an Automated Diagnostics System for Eddy Current Analysis Using Applied Artificial Intelligence Techniques

The purpose of this research project is to investigate and integrate large database management methods, artificial neural network technology, and fuzzy logic decision making to develop a diagnostics system for steam generator tubing anomaly detection using eddy current test data. The following issues are being considered: 1. Digital data calibration, compression and representation. 2. Development of modular neural networks for defect classification and defect parameter estimation. 3. Development of fuzzy logic decision-making a scheme.

4. Development of an expert system for database management, compilation of a trained neural network library, and a module for on-line decision making. 5. Development of guidelines for the implementation of this technology.

Title: Life Prediction of Plant Components

The purpose of the predictive maintenance technology research is to develop practical approaches to enhance solutions to industrial maintenance problems. Models of system degradation, advanced data processing, laboratory experimentation and field data are being integrated to achieve specific objectives. These include incipient detection of system faults, estimation of residual life of plant components, determination of 'time-to-alarm' and 'time-to-failure', establishing an alarm level based

on the variations of a physical parameter (if necessary, by the prediction of a 'virtual trend'), and the development of a generic procedure for application to both rotating machinery and stationary components. Probabilistic approaches are being developed for establishing a relationship between lifetime and alarm levels.

Title: Development of a Local Sensor Fault Detection Methodology

Sensor failure modes and their effects were reviewed through literature searches along with various sensor fault detection methods that were previously studied or implemented. In the current approach a methodology for local sensor fault detection was developed using raw data analysis that might be used for on-board implementation on a sensor. Computational optimization is a key to the success of this approach. This methodology uses signatures such as block mean, block variance and signal rate of change to produce binary decisions using a set of if-then rules. Artificial and real sensory data were used to test the performance of this new computer software. A statistical uncertainty analysis was performed to assess the confidence factors related to the decisions made by the system regarding sensor quality. The results of applications to various process sensors (pressure, temperature) from TVA's Kingston Fossil Plant indicate the feasibility of the current approach to industrial sensors. In addition to the raw data analysis, signal representation using univariate autoregression (AR) models was also investigated. The residual sequence calculated from the model was used to detect various fault types. Furthermore, the AR model was also used to monitor sensor time constant and ramp delay time.

Title: Aging and Prognosis of Temperature Detectors

The purpose of this research is to develop a life assessment method for estimating the residual life of resistance temperature detectors (RTDs). The normal aging of RTDs occurs because of exposure to heat, thermal cycling and environmental effects such as humidity, vibration and mechanical shock. The effect of temperature is the most significant factor in reducing the life of RTDs. The calibration drift may be determined by measuring the sensing element resistance, the loop resistance and the compensating loop resistance. Moisture in the RTD reduces the insulation resistance and causes calibration errors. Two approaches are being developed to estimate the residual life of RTDs. These are: 1. Adaptive dynamic regression models. 2. Artificial neural network models. Various forms of these models will be evaluated using laboratory test data. The residual life or the calibration life will be estimated as a function of the measured RTD resistance. RTDs are being tested in the laboratory at high temperatures and other aggravating conditions and degradation data will be acquired.

Title: Smart Maintenance Technology

Technology that would enhance the capability of machinery to self-diagnose is often called "smart maintenance." This project involves the integration of multi-sensor fusion, predictive models for machine prognosis, and on-line and distributed processing of information from on-board sensors. The processed information may be used at the machine level by the plant personnel, or it could be broadcast to a central facility. An experimental information processing card has been interfaced with sensors to measure vibration and electrical signals. The DSP card consists of an EEPROM for permanent storage of processing code. It is envisioned that machinery in the future will be manufactured with built-in sensors and on-board distributed processing of diagnostic data, that may be used for incipient fault detection and remediation.