Industrial IoT-driven condition-based maintenance plus for complex system with multiple dependencies

Maintenance management based on industrial Internet of Things (IoT) can significantly increase the efficiency of complex system maintenance and enable a transformation from reactive response to proactive prevention. However, conventional condition-based maintenance (CBM) tends to be single-component independent maintenance, which cannot perform collaborative optimization of multi-component maintenance and resource scheduling. In addition, due to shared resources and sequential workflows, the dependencies of the maintenance processes between components makes the existing CBM models not applicable. To solve the aforementioned problem, this paper proposes an industrial IoT-driven condition-based maintenance plus (CBM+) method allowing to perform collaborative optimization of proactive maintenance activities for complex systems. Firstly, the real-time remaining useful life of components is predicted based on degradation data monitored by IoT sensors. Secondly, considering the economic-functional-maintenance process dependencies, a multi-component opportunistic maintenance strategy, based on hierarchical Bayesian networks and multi-layer maintenance process networks, is proposed to increase the collaborative maintenance capacity. Afterwards, leveraging an industrial IoT-enhanced field management approach, the maintenance elements (human, equipment, material, method, and environment) are systematically managed to optimize the maintenance efficiency. Furthermore, an industrial IoT-driven CBM+ optimization model considering multiple dependencies and maintenance elements is developed. Finally, a case study of an industrial IoT-driven hydraulic system is conducted to demonstrate the proposed maintenance strategy.