Multivariate/minor fault diagnosis with severity level based on Bayesian decision theory and multidimensional RBC

Abstract

Fault diagnosis(FD) is vital for monitoring of industrial processes. Actually, both the multiple fault variables and minor faults are inclined to bring wrong diagnosis results. In this paper, a fault variable identification method is proposed for multivariate/minor fault diagnosis. The deviation factor is adopted as the characteristic of the sample, and Bayesian decision theory is adopted to calculate the possibility of the variable being faulty, then multi-dimensional reconstruction-based contribution (MRBC) is used to determine the fault source variables. This method not only improves the diagnosis rate for multiple/minor faults with less computation time, but also can indicate severity level of the different fault variables according to the fault occurrence probability. A Numerical example and Tennessee Eastman process are given to show the efficiency of the proposed method.