Data-driven root-cause fault diagnosis for multivariate non-linear processes

In a majority of multivariate processes, propagating nature of malfunctions makes the fault diagnosis a challenging task. This paper presents a novel data-driven strategy for real-time root-cause fault diagnosis in multivariate (non-)linear processes by estimating the strength of causality using normalized transfer entropy (NTE) between measured process variables and variations of a residual signal. In this paper, a new framework for root-cause fault diagnosis applicable for multivariate nonlinear processes is proposed, which can reduce the necessary number of calculation for causality analysis among time-series. More specially, a new and fast symbolic dynamic-based normalized transfer entropy (SDNTE) technique is proposed to enable real-time application of transfer entropy, which has been considered as a burdensome approach for causality analysis. The concept of SDNTE is built upon principles of time-series symbolization, xD-Markov machine and Shannon entropy. This paper also introduces a new concept of joint xD-Markov machine to capture dynamic interactions between two time-series. The proposed root-cause fault diagnosis framework is applied on Tennessee Eastman process benchmark and its computational advantages are shown by comparing with conventional kernel PDF-based method. Moreover, the proposed strategy is applied to health monitoring of a big scale industry centrifuge to corroborates its effectiveness and feasibility in industrial applications.