Enhancing Smart Manufacturing with Secure Predictive Maintenance: a Data-Driven Approach

In smart manufacturing systems, Predictive Maintenance (PdM) is essential for optimizing equipment performance and reducing unplanned downtime for automated systems and machinery. Managing the vast amounts of real-time operational data generated by these systems requires a secure, scalable data-driven approach for robust PdM. This paper introduces a secure PdM framework employing Hyperbolic Cosine Differential Privacy (HCDP) to enhance attribute privacy in big datadriven predictive maintenance. Specifically, the system handles large-scale data acquisition, pre-processing, data grouping, and attribute extraction, with key attributes protected by HCDP. Next, the PdM analysis is conducted using Transfer Learning Generalized Collapsing Long Nuclear Logarithmic Entropy Short Term Memory (TL-GCLNLESTM), a model that leverages transfer learning to adapt to diverse scenarios and identify patterns within massive data streams ensuring predictive security. Then the Local Interpretable Model-agnostic Schwefel Explanation (LIMSE) framework interprets predictive outcomes, enhancing transparency and decision-making. If a failure is predicted/anticipated, the maintenance schedule is recomended using Ramp Multi-peak Gaussian Fuzzy (RMG-Fuzzy), with alerts issued accordingly. Experimental results demonstrate that the proposed model achieves high accuracy showcasing the efficacy of the proposed approach for PdM in smart manufacturing maintenance.