AI-Based Predictive Maintenance Model for Industrial Systems
TL;DR
This paper proposes an end-to-end explainable predictive maintenance framework consisting of a contextual probabilistic prognosticator based on convolutional and self-attention networks and a SHAP-based attribution and a risk-sensitive decision layer that is formulated as either a mixed-integer linear programming or distributional reinforcement learning problem.
Abstract
The occasional failure of industrial systems' equipment leads to significant production losses, increased operating expenses and safety risks while high-frequency data from the sensors makes data-driven prognostics possible, the existing approaches often consider the remaining useful life estimation (RUL), explainability and maintenance decision-making as distinct phases, are only able to make point predictions with no reliable uncertainty quantification and are not able to leverage the operational context. In this paper, we propose an end-to-end explainable predictive maintenance framework consisting of a contextual probabilistic prognosticator based on convolutional and self-attention networks and a SHAP-based attribution and a risk-sensitive decision layer that is formulated as either a mixed-integer linear programming or distributional reinforcement learning problem. The framework achieves a root mean square error (RMSE) remaining useful life error of 11.3, Area Under the ROC curve (AUROC) for short horizon failure probability of 0.96, 22 percent reduction in unplanned downtime and 15 percent reduction in maintenance cost over strong baselines on the NASA C-MAPSS turbofan engine dataset. This method provides precise forecasts, steady explanations and schedules feasible within resources, promoting trustworthy and economically competent predictive maintenance for Industry 4.0 systems.