Fault detection via nonlinear profile monitoring using artificial neural networks

Abstract

Abstract Fault detection is the characterization of a normal behavior of a system using a response function or profile of interest and the identification of any deviation from such normal behavior. As system complexity grows, predicting the underlying structure or form of response function becomes challenging if not impossible. This article presents a data‐driven approach for fault detection of complex systems using multivariate statistical process control based on artificial neural network (ANN) characterization. In this approach, the quality of a system is characterized where one explanatory variable is adequately explained as a function of the other variables using an ANN model. The vector of weights and biases of the ANN model is monitored by using Hotelling T 2 through control charts. The proposed method is tested and compared with existing methods such as polynomial and sum of sine function regression for 3 cases from the literature. Moreover, it is applied to a 4‐story reinforced concrete building that uses continuous monitoring to avoid potentially catastrophic failures. The proposed ANN approach outperforms the existing methods for small shifts (deviations) from healthy states. For large and medium shifts, it provides comparable results that are on the conservative side.