A neural-network approach to fault detection and diagnosis in industrial processes

Using a multilayered feedforward neural-network approach, the detection and diagnosis of faults in industrial processes that requires observing multiple data simultaneously are studied in this paper. The main feature of our approach is that the detection of the faults occurs during transient periods of operation of the process. A two-stage neural network is proposed as the basic structure of the detection system. The first stage of the network detects the dynamic trend of each measurement, and the second stage of the network detects and diagnoses the faults. The potential of this approach is demonstrated in simulation using a model of a continuously well-stirred tank reactor. The neural-network-based method successfully detects and diagnoses pretrained faults during transient periods and can also generalize properly. Finally, a comparison with a model-based method is presented.

[1]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[2]  William L. Luyben,et al.  Process Modeling, Simulation and Control for Chemical Engineers , 1973 .

[3]  David Mautner Himmelblau,et al.  Fault detection and diagnosis in chemical and petrochemical processes , 1978 .

[4]  Sunwon Park,et al.  FAULT-DETECTION AND DIAGNOSIS VIA PARAMETER-ESTIMATION IN LUMPED DYNAMIC-SYSTEMS , 1983 .

[5]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[6]  J. F. Davis,et al.  An expert system approach to malfunction diagnosis in chemical plants , 1988 .

[7]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[8]  Masahiro Abe,et al.  Incipient fault diagnosis of chemical processes via artificial neural networks , 1989 .

[9]  Venkat Venkatasubramanian,et al.  A neural network methodology for process fault diagnosis , 1989 .

[10]  Ramaswamy Vaidyanathan,et al.  Process fault detection and diagnosis using neural networks , 1990 .

[11]  Josiah C. Hoskins,et al.  Artificial neural network models for knowledge representation in chemical engineering , 1990 .

[12]  R. Li,et al.  Dynamic fault detection and diagnosis using neural networks , 1990, Proceedings. 5th IEEE International Symposium on Intelligent Control 1990.

[13]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[14]  Kenneth A. Loparo,et al.  Leak detection in an experimental heat exchanger process: a multiple model approach , 1991 .

[15]  Ruokang Li,et al.  Fault detection and diagnosis in a closed-loop nonlinear distillation process: application of extended Kalman filters , 1991 .

[16]  David M. Himmelblau,et al.  FAULT DIAGNOSIS IN COMPLEX CHEMICAL PLANTS USING ARTIFICIAL NEURAL NETWORKS , 1991 .

[17]  Bill Curtis,et al.  Process modeling , 1992, CACM.

[18]  Hiroshi Seki,et al.  Abnormal Event Identification in Nuclear Power Plants Using a Neural Network and Knowledge Processing , 1993 .