Neural networks in process fault diagnosis

Abstract

Fault detection and diagnosis is an important problem in process automation. Both model-based methods and expert systems have been suggested to solve the problem, along with the pattern recognition approach. A number of possible neural network architectures for fault diagnosis are studied. The multilayer perceptron network with a hyperbolic tangent as the nonlinear element seems best suited for the task. As a test case, a realistic heat exchanger-continuous stirred tank reactor system is studied. The system has 14 noisy measurements and 10 faults. The proposed neural network was able to learn the faults in under 3000 training cycles and then to detect and classify the faults correctly. Principal component analysis is used to illustrate the fault diagnosis problem in question.<>