Early Discovery of Failing Equipment and Sensors in Power Plants using Advanced Pattern Recognition

摘要

This study describes the application of Advanced Pattern Recognition On-line Monitoring in a power plant fleet environment. Advanced Pattern Recognition has been able to successfully and routinely detect incipient equipment failures and sensor failures not typically detected by traditional control systems. Typical failure identification and alarming is carried out applying high and low limits to the range of a single instrument measurement. This results in a rather wide fixed window of operation. Advanced Pattern Recognition technology has been employed to monitor multiple sensor systems using a moving window to identify normal and abnormal operation. Using a moving window takes into account the current operating conditions of the equipment and allows the early detection of many types of failures. The moving window is defined by a pattern recognition generated expected value and the threshold or normal range about that expected value. These abnormalities are then compared to typical failure signatures to suggest the failure origin. The expected value can also be used to validate the signal and provide a replacement signal value. Practical applications of advanced pattern recognition to detect plant equipment and sensor failures will be described. The variety of incipient equipment failures, which have been identified in real plant environments, will be categorized. Several failures will be illustrated using case studies. Subtle and difficult to detect failures will be profiled. These will also be illustrated by case studies. Advanced pattern recognition has been used successfully in plant environments to provide the early detection of equipment and sensor failures.