System-Level Predictive Maintenance Optimization for No-Wait Production Machine–Robot Collaborative Environment under Economic Dependency and Hybrid Fault Mode

Abstract

For manufacturing systems such as hot rolling, where there is no wait in the production process, breaks between adjacent production batches provide “opportunities” for predictive maintenance. With the extensive application of industrial robots, a production machine–robot collaboration mode should be considered in system-level predictive maintenance. The hybrid failure mode of machines and dependencies among machines further elevate the difficulty of developing predictive maintenance schedules. Therefore, a novel system-level predictive maintenance method for the no-wait production machine–robot collaborative maintenance problem (NWPMRCMP) is proposed. The machine-level predictive maintenance optimization model under hybrid failure mode, which consists of degradation and sudden failure, is constructed. Based on this, the system-level maintenance optimization model is developed, which takes into account the economic dependency among machines. The maintenance model with the objective of minimizing the total cost is transformed into a Markov decision process (MDP), and a tailored proximal policy optimization algorithm is developed to solve the resulting MDP. Finally, a case study of a manufacturing system consisting of multiple hot-rolling machines and labeling robots is constructed to demonstrate the effectiveness of the proposed method. The results show that the designed algorithm has good performance and stability. Moreover, the developed strategy maximizes the performance of the machine and thus reduces the total maintenance cost.