A wavelet neural network assisted framework for active fault detection and diagnosis of process systems

Modern day industries invest heavily on looking for various methods for timely and accurate fault detection and diagnosis. Since training a model to learn all possible faults is challenging and impractical, developing an active learning based methodology which is capable of learning about any new faults arriving in the plant in the due course of operation is the main objective of this paper. This objective is achieved through a two staged methodology where in, an unsupervised learning strategy using one-class SVM is considered in the first stage to detect the presence of a new fault. In the second stage a multi-class classifier of Wavelet Neural Network is utilized to detect the nature of fault. The efficacy of the proposed method is demonstrated on a benchmark Tennessee Eastman Process and the results are compared with the existing methods.