Explainability for Fault Detection System in Chemical Processes

TL;DR

This paper compares two XAI methods, IG and SHAP, to explain fault detection decisions of an LSTM classifier in a chemical process, demonstrating their effectiveness in identifying fault subsystems and their potential for broader applications.

Contribution

The study applies and compares IG and SHAP XAI methods to fault diagnosis in chemical processes, highlighting their ability to identify fault origins and their general applicability.

Findings

SHAP often provides more detailed explanations.

Both methods successfully identify fault-related features.

The approach is applicable to other processes.

Abstract

In this work, we apply and compare two state-of-the-art eXplainability Artificial Intelligence (XAI) methods, the Integrated Gradients (IG) and the SHapley Additive exPlanations (SHAP), that explain the fault diagnosis decisions of a highly accurate Long Short-Time Memory (LSTM) classifier. The classifier is trained to detect faults in a benchmark non-linear chemical process, the Tennessee Eastman Process (TEP). It is highlighted how XAI methods can help identify the subsystem of the process where the fault occurred. Using our knowledge of the process, we note that in most cases the same features are indicated as the most important for the decision, while insome cases the SHAP method seems to be more informative and closer to the root cause of the fault. Finally, since the used XAI methods are model-agnostic, the proposed approach is not limited to the specific process and can also be used in similar problems.