Explainable and Trustworthy AI for Fault Classification in the Tennessee Eastman Process: A Step Toward Industrial Autonomy

Abstract

Achieving higher levels of Industrial Autonomy (IA) requires fault diagnostic systems that combine predictive accuracy with transparent decision-making. In safety-critical process industries like petroleum refinery, black-box AI models often face adoption barriers due to limited interpretability. The work introduces a glass-box fault classification framework for the Tennessee Eastman Process, comparing a baseline direct-modeling approach with a novel dual-branch architecture. The proposed method decomposes process parameters into trend and cyclic components, trains dedicated classifiers on each and fuses their probabilistic outputs. The proposed design improves sensitivity to both gradual drifts and oscillatory anomalies. In the present work SHAP explainability is incorporated to provide global, local, and class-wise feature attribution, enabling operators to trace model reasoning and align diagnostics with process knowledge. A strong industrial AI platform, purpose-built for domain engineers, emerges as essential for operationalizing such capabilities, empowering process experts to directly harness AI for decision-making. The present work serves as a steppingstone toward realizing such an Industrial AI platform, demonstrating how interpretable AI can bridge the gap between advanced analytics and domain expertise. The experimental evaluation of the proposed technique demonstrates that 35% of the fault classes achieved improved accuracy, with an average accuracy gain of 4.34% over the baseline, with pronounced gains in cyclic-dominated faults. The approach demonstrates a pathway toward Level 5 IA by delivering interpretable, high-performance fault diagnostics ready for real-time deployment.