XFDDC: eXplainable Fault Detection Diagnosis and Correction framework for chemical process systems

Abstract

Industry 4.0 process fault detection and diagnosis(FDD) is built on the foundations of Industrial Internet of Things(IIoT) for sensing and artificial intelligence for recognizing patterns. Despite the performance and application of various learning-based algorithms in multiple sectors, their black-box nature makes industrial experts skeptical. So, this paper proposes an eXplainable Fault Detection, Diagnosis, and Correction(XFDDC) Framework to create best-fit FDD models that are explainable. The XFDDC framework is designed to explain the FDD model predictions using eXplainable Artificial Intelligence(XAI) techniques. The proposed framework was applied to the bench-marked Tennessee Eastman Process(TEP) dataset. On evaluation, an XGBoost model yielded better Fault Detection Rate(FDR) and F1 score against popular transparent and complex models like Naive Bayes, K-Nearest Neighbors, Random Forest and a rule-based version of XGBoost. To explain the predictions of the XGBoost model, the XFDDC framework suggests the use of feature-based XAI techniques. So, the TreeSHAP algorithm is applied on the XGBoost model to generate local and global explanations as part of fault diagnosis. The proposed framework also recommends counterfactual explanations to provide action recommendations for correcting the fault situation. Thus, a best-fit explainable XGBoost Fault Detection, Diagnosis, and Correction (XGBoost-XFDDC) model is created.