BrakeGuard-XAI – An Advanced Secure Explainable AI Paradigm For Early-Stage Brake Anomaly Detection And Interpretable Predictive Maintenance

The study suggests an accessible and secure machine learning model for forecasting brake failures in large commercial vehicles. We support this proposal with evidence. Heavy transport vehicles\\\' Air Pressure System (APS) is constantly monitored by IoT-based sensors in modern day heavy transport systems, generating vast amounts of operational data. Detecting brake failures manually with large and highly unbalanced datasets is time-consuming and inefficient. Our approach to these problems involves the use of K-Nearest Neighbour (KNN) imputation for missing values and SMOTE for dealing with class imbalance. Both methods are effective in both situations. Logistic Regression, Decision Tree, Support Vector Machine, Gradient Boosting, and Random Forest are among the machine learning algorithms that undergo stratified cross-validation during implementation and evaluation. The Random Forest classifier\\\'s accuracy, precision, recall, F1-score and ROC-AUC are shown to be more than satisfactory using experimental data. Enhanced transparency and trust in the prediction process are achieved through the use of Explainable Artificial Intelligence (XAI) techniques like SHAP and LIME, which can interpret model decisions. They also use methods of selecting features that reduce computational complexity while preserving high levels of accuracy in making predictions. This proposed framework improves fault detection reliability, reduces maintenance costs and allows for predictive maintenance in heavy transport systems.