Explainable AI for Multi-Class Machine Failure Prediction : A Focus on Calibrated Explainability and Model Uncertainty

Predictive maintenance has become essential in modern manufacturing, aiming to prevent costlyand potentially hazardous equipment failures. Traditional maintenance strategies are increasingly beingreplaced by AI-driven predictive models. However, despite their accuracy, many of these modelsoperate as black boxes, offering limited insight into their decision-making processes and thereby undermininguser trust. This study proposes a novel framework that combines Explainable AI (XAI) andUncertainty Quantification (UQ) to enhance both the interpretability and trustworthiness of multi-classfailure predictions. We extend the Venn–Abers-based Calibrated Explanations (CE) method to improveits support for multi-class classification. The framework is evaluated using four industrial datasets andbenchmarked against established models and explanation techniques, including SHapley Additive ex-Planations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). A Gated RecurrentUnit (GRU) model was selected for its effectiveness in handling temporal data and consistently outperformedtraditional machine learning models across several classification evaluation metrics. Surprisingly,calibration techniques such as Platt Scaling, Isotonic Regression, and Venn–Abers did notreliably calibrate the GRU’s probabilistic outputs, suggesting that deep learning models may exhibitinherent calibration in certain contexts. The extended CE method provided the most robust and stableexplanations, outperforming SHAP and LIME in variance-based evaluation metrics. A within-subjectsurvey involving 22 participants with engineering backgrounds showed that explanations combiningprediction probabilities, feature importance, and uncertainty led to higher trust ratings and more informeddecision-making. Participants preferred CE-based explanations and exhibited greater confidenceand contextual awareness in their decisions, particularly when faced with trade-offs betweenproduction costs and failure risks.