INCIPIENT FAULT-DETECTION AND IDENTIFICATION IN-PROCESS SYSTEMS USINGACCELERATED NEURAL-NETWORK LEARNING

The objective of this paper is to present the development and numerica l testing of a robust fault detection and identification (FDI) system using artificial neural networks (ANNs), for incipient (slowly develop ing) faults occurring in process systems. The challenge in using ANNs in FDI systems arises because of one's desire to detect faults of vary ing severity, faults from noisy sensors, and multiple simultaneous fau lts. To address these issues, it becomes essential to have a learning algorithm that ensures quick convergence to a high level of accuracy. A recently developed accelerated learning algorithm, namely a form of an adaptive back propagation (ABP) algorithm, is used for this purpose . The ABP algorithm is used for the development of an FDI system for a process composed of a direct current motor, a centrifugal pump, and t he associated piping system. Simulation studies indicate that the FDI system has significantly high sensitivity to incipient fault severity, while exhibiting insensitivity to sensor noise. For multiple simultan eous faults, the FDI system detects the fault with the predominant sig nature. The major limitation of the developed FDI system is encountere d when it is subjected to simultaneous faults with similar signatures. During such faults, the inherent limitation of pattern-recognition-ba sed FDI methods becomes apparent. Thus, alternate, more sophisticated FDI methods become necessary to address such problems. Even though the effectiveness of pattern-recognition-based FDI methods using ANNs has been demonstrated, further testing using real-world data is necessary .