Sparse Adjacency Forecasting and Its Application to Efficient Root Cause Diagnosis of Process Faults

Abstract

Abstract Efficient fault root cause diagnosis is essential to ensure the production safety of industrial processes. The existing root cause diagnosis models can be summarized as linear methods and nonlinear methods. Linear methods cannot handle nonlinear processes well, while nonlinear methods usually require pairwise calculations between variables, which are complex and difficult to apply in real time. To address the above issues, a method for root cause diagnosis of nonlinear processes, termed sparse adjacency forecasting (SAF), is proposed in this paper. SAF is a causal inference method based on the idea of Granger causality. While forecasting time series, it constructs an adjacency matrix to synthesize the process information and the interaction of different variables. By adding sparse constraints to the adjacency matrix, the predictive effects between variables are reflected, and the causality is captured. This method only needs to model once to obtain the causal relationship between all variables, which avoids multiple modeling and improves diagnosis efficiency. Besides, in order to solve the nonlinear problem, multiple nonlinear random feature nodes are introduced for time series prediction. Two cases are adopted to verify the causal inference and root cause diagnosis performance of the proposed method, including a numerical case and the Tennessee Eastman benchmark process.