A novel explainable propagation-based fault diagnosis approach for Clean-In-Place by establishing Boolean network model

Abstract

Industrial processes usually exhibit the strong logical relationships between different components, which can be accomplished and exhibited by Boolean functions. On this foundation, we develop an approach based on Boolean network (BN) to achieve fault diagnosis for binary industrial processes by applying the semi-tensor product (STP). At first, Boolean control network model for the binary industrial process and the corresponding fault propagation BN model are established. A fault propagation observer is introduced to select out the component nodes from the fault propagation BN and obtain the fault propagation path. Based on this, the definition of fault diagnosability is given, and a novel fault diagnosis approach is proposed to trace the fault source and predict the final state of fault propagation. After that, a novel metric based on the logical complexity of fault propagation is introduced to evaluate the explainability of proposed fault diagnosis approach. Finally, the proposed approach is applied in traditional Chinese medicine concentration tank Clean-In-Place to demonstrate its effectiveness and explainability.