Data Mining with Explainable Deep Representation Models for Predicting Equipment Failures in Smart Manufacturing Environments

The increasing complexity of smart manufacturing environments demands predictive maintenance systems capable of detecting equipment failures before they disrupt operations. Data mining integrated with explainable deep representation models offers a powerful approach for extracting actionable insights from high-dimensional, heterogeneous industrial data. This method combines the pattern recognition capabilities of deep learning with the interpretability required for trust and operational transparency in decision-making. In the proposed framework, multi-source manufacturing data including sensor readings, operational logs, environmental conditions, and historical maintenance records are processed through deep representation models such as autoencoders and graph neural networks. These models learn compact, meaningful feature embeddings that capture temporal and spatial correlations indicative of impending failures. The explainability layer employs techniques such as SHAP (Shapley Additive Explanations) and Layer-wise Relevance Propagation to attribute model outputs to specific input variables, allowing maintenance teams to understand why a prediction was made. By integrating advanced data mining workflows, the system can identify recurrent fault patterns, reveal hidden dependencies among process variables, and adapt to evolving manufacturing configurations. The explainability mechanisms also enhance human–AI collaboration, enabling engineers to validate model reasoning and integrate domain expertise into predictive strategies. This reduces false positives, increases trust in automated predictions, and accelerates fault diagnosis. The approach supports both real-time failure prediction for operational continuity and long-term asset health monitoring for strategic planning. By uniting predictive accuracy with interpretability, it addresses one of the primary barriers to deploying AI in industrial environments ensuring reliability without sacrificing transparency. This dual focus enables smart manufacturing systems to achieve higher uptime, optimise maintenance schedules, and reduce overall operational costs.