Incipient fault detection and diagnosis using artificial neural networks

Abstract

Fault is defined as degradation between 100% performance and complete failure. The authors demonstrate how an artificial neural network can detect and diagnose faults from online process data. A wide range of input patterns can be learned by artificial neural networks in the presence of noise by changing the interconnections of the nodes, their thresholds for activation, and their individual weights. Artificial neural networks are able to take inputs from the processes without knowing the process model, allowing representation of complex engineering systems which are difficult to model either with traditional model-based engineering or knowledge-based expert systems. A description is given of some of the characters of a neural network that are useful for fault discrimination in a chemical plant. It is shown that even when using noisy sensor data, the misclassification rate is nil