Intelligent Maintenance Framework for Reconfigurable Manufacturing With Deep-Learning-Based Prognostics

Future reconfigurable manufacturing systems (RMSs) can dynamically modify the system structures to achieve personalization, customization, and consumer-maker co-creation. The advanced Internet of Things (IoT) integrating deep learning and intelligent maintenance is critical for ensuring the operation and maintenance of RMS. On the one hand, a multihead neural network is developed under the variability of individual machine degradations for deriving machine-level prognostics. It learns degradation features with superior generalization performance by simultaneously fitting multiple candidate distributions and updates remaining useful lifetime (RUL) distributions from diversified distribution ensembles. On the other hand, a flexible opportunistic maintenance policy is proposed to optimize the dynamic maintenance scheduling for the multiphase RMS by utilizing real-time updated RUL distributions. Meanwhile, considering the unique attributes of RMSs, maintenance opportunities that arise from the in-situ machine predictive maintenance, system structure, and sequential reconstruction time are fully utilized. This IoT-enabled prognostics and opportunistic maintenance (POM) framework can achieve a bi-level interactive mechanism and inner/outer loops to reduce decision-making complexity and maintenance costs for future reconfigurable manufacturing. Numerical experiments demonstrate that this framework has superior predictive performance and significant cost savings to empower intelligent maintenance.