Incipient Fault Detection and Identification in Process Systems Using Accelerated Neural Network Learning
| Autor: | Alexander G. Parlos, J. Muthusami, Amir F. Atiya |
|---|---|
| Rok vydání: | 1994 |
| Předmět: |
Nuclear and High Energy Physics
Artificial neural network Noise (signal processing) Computer science 020209 energy Reliability (computer networking) Process (computing) Control engineering 02 engineering and technology Work in process Condensed Matter Physics Fault (power engineering) Backpropagation 020303 mechanical engineering & transports 0203 mechanical engineering Nuclear Energy and Engineering 0202 electrical engineering electronic engineering information engineering Sensitivity (control systems) |
| Zdroj: | Nuclear Technology. 105:145-161 |
| ISSN: | 1943-7471 0029-5450 |
| DOI: | 10.13182/nt94-a34919 |
| Popis: | The objective of this paper is to present the development and numerical testing of a robust fault detection and identification (FDI) system using artificial neural networks (ANNs), for incipient (slowly developing) faults occurring in process systems. The challenge in using ANNs in FDI systems arises because of one's desire to detect faults of varying severity, faults from noisy sensors, and multiple simultaneous faults. 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 the 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 simultaneous faults, the FDI system detects the fault with the predominant signature. The major limitation of the developed FDI system is encountered when it is subjected to simultaneous faults with similar signatures. During such faults, the inherent limitation of pattern-recognition-based FDI methodsmore » 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.« less |
| Databáze: | OpenAIRE |
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