A Hybrid Ensemble and Explainable AI Framework for Predictive Maintenance of Industrial Equipment

Abstract

A modern industrial system with its critical machinery is very sensitive to unexpected equipment failure and may experience extensive operation interruption, danger to safety, and cost. The traditional maintenance approaches, reactive and preventative, lack intelligence and flexibility to make predictions of the failure based on real-time information, leading to failures that are expensive to fix or unnecessary maintenance. This paper proposes a hybrid ensemble predictive maintenance(PdM) framework to assist in overcoming these drawbacks by combining potent machine learning (ML) models as classifiers to PdM and SHapley Additive eXplanation (SHAP) framework to make decision-making PdM transparent and interpretable. The suggested approach is trained on actual industrial sensor data comprising multivariate time-series data such as temperature, vibration, voltage, and pressure measurements. Data are preprocessed in a powerful way with the removal of redundancy, label encoding, and scaling. The accuracy, precision, recall, F1-score, and analysis of the confusion matrix are used to evaluate each model. Strikingly, the three ensemble classifiers had 100 percent success in the detection of faults, with SHAP values having obvious key features dictating forecasts. The newness of this method is that it is potentially high-accuracy and interpretable at the same time, which is a respite from deeper or federated learning models, which are typically high-computational-load methods. The study adds a scalable, accurate, and explainable PdM framework that can be part of the new smart manufacturing.