Explainable Bayesian-Optimized XGBoost Model for Component Failure Detection in Predictive Maintenance

Abstract

This study employs predictive maintenance to enhance the Explainable XGBoost Model for predicting failures in industrial components. The research utilizes a model that employs the adaptive sliding window approach to extract features. The timeframe for this approach is set at 24 hours. This method leverages multi-device sensor data to extract the features. The BO-XGBoost model is assessed using accuracy, MCC, F1-Score, and G-mean. The measures achieved 99.87%, 0.988, 0.990, and 0.0989, respectively. The SHAP analysis method also identifies the characteristics of target variable prediction. The variable “rotatemean_24” is the most significant predictor. The model is easily understandable, aiding in comprehending relationships between features and outcomes. Therefore, it can assist in making decisions regarding predictive maintenance. Research has shown that implementing the optimized Explainable XGBoost Model can enhance factory maintenance efficiency and cost-effectiveness. The model's predictive capabilities enable proactive maintenance by identifying machine faults in advance.