EXPLAINABLE FAULT DIAGNOSIS FOR INDUSTRIAL MACHINERY USING DATA-DRIVEN MODELING

Abstract

Industrial machinery reliability is essential for maintaining productivity, safety, and operational efficiency in modern manufacturing environments. Traditional fault diagnosis approaches often depend on expert knowledge, predefined rules, and manual inspection methods, which may not effectively handle complex equipment behaviors and large-scale sensor data. This paper proposes an Explainable Fault Diagnosis framework for industrial machinery using data-driven modeling by integrating Industrial Internet of Things (IIoT), machine learning, sensor analytics, explainable artificial intelligence (XAI), and predictive modeling techniques. The proposed methodology collects real-time equipment data, extracts meaningful operational features, identifies abnormal conditions, classifies machine faults, and provides interpretable diagnostic explanations. The framework improves fault detection accuracy while increasing transparency and trust in AI-driven maintenance decisions. Experimental evaluation demonstrates improvements in diagnostic performance, interpretability, response efficiency, equipment reliability, and maintenance planning. The proposed approach provides a scalable and trustworthy solution for intelligent fault diagnosis in Industry 4.0 manufacturing environments. Keywords— Explainable AI, Fault Diagnosis, Industrial Machinery, Data-Driven Modeling, IIoT, Predictive Maintenance, Machine Learning, Smart Manufacturing.