A Smart Industrial IoT Monitoring Framework Using Ensemble Learning and Explainable AI for Equipment Failure Prediction

Abstract

Unexpected equipment failures are known to be one of the most critical causes of production losses. Such failures can result in increased maintenance cost, compromise safety of operators and increase operational inefficiency. Therefore, there is a growing interest to monitor equipment during normal operation and predict possible failure before it actually occurs. This paper describes a smart Industrial Internet of Things (IIoT) framework to monitor industrial equipment in real time using ensemble learning for failure prediction. The framework initially collects a variety of real time operating parameters of industrial equipment such as temperature, vibration, speed, torque, pressure, etc from various industrial sensors. The collected data is then preprocessed using techniques such as treatment of missing values, noise removal, feature scaling, handling of class imbalance and feature selection to select most relevant features. The preprocessed data is then fed into various machine learning models such as random forest, support vector machine, gradient boosting and multi layer perceptron. A stacking-based ensemble model is used to combine the predictions of individual models to improve the overall prediction accuracy and stability. The final output of the framework is a classification of equipment condition into normal or failure-prone, along with a probability of failure. Shapley Additive explanations (SHAP) is used to provide both global and local interpretability to the predictions made by the model. The performance of the model is evaluated using various metrics such as accuracy, precision, recall, F1-score, area under receiver operating characteristic curve, false-alarm rate and inference time. The framework has potential to provide better failure detection using combination of IIoT monitoring and explainable AI, and provide understandable reasons for failure to maintenance personnel responsible for maintenance.