A Novel Propagation Path Identification Framework for Faults in Industrial Processes

Abstract

In modern industry, timely and accurate fault diagnosis plays an important role in satisfying the demands of production safety and stability of production quality. This paper dedicates on propagation path identification of faults in industrial processes, which will offer a feasible technology or solution to take corrective and timely maintenance measures for field engineers. Specifically, a recurrent neural networks-based Granger causality analysis approach is developed, which has sufficiently considered the nonlinear and dynamic relationships among time series after faults happen. Finally, we validate our approach on a typical industrial process, finishing mill process, to demonstrate the efficiency of the proposed scheme.