Root Cause Diagnosis Framework Based on Granger Causality with the Combination of Normal and Fault Data

Abstract

Granger causality analysis is one of the most widely used methods in root cause diagnosis. This method can get effective results in many cases, but there are still some problems and underutilization of data is one of them. Granger causality analysis only used the fault relate data segment. This paper proposes a novel root cause diagnosis framework based on Granger causality analysis, and attempts to combine the normal and fault data to make the result more accurate. The main ideal is to test the change of causality intensity before and after the fault to optimize the result of the fault propagation paths. Tennessee Eastman(TE) process data and TE data was used to verify the effectiveness of the method.