Equipment-centric Data-driven Reliability Assessment of Complex Manufacturing Systems

Complex manufacturing systems produce highly engineered products with long product cycle times and are characterized by complex production process behaviors. Ensuring the reliability of these systems is critical to meet customer demands, improve product quality and minimize production losses. The collection and storage of data by sensors and information systems respectively enable the automatic generation and analysis of reliability models of complex manufacturing systems, reducing the need for expert knowledge of the processes. In this article, we propose a novel approach to generate data-driven reliability models of complex manufacturing systems using stochastic Petri nets as the modeling formalism. Our method extracts models from event logs that capture relevant events related to material flow in a system, and state logs, that capture operational state changes in a system's production resources using process mining. We, furthermore, simulate the derived data-driven reliability models using discrete-event simulation and validate the models to ensure their robustness. We demonstrate the successful application of our method using a case study from the wafer fabrication domain. The results of our case study indicate that data-driven reliability assessment of complex manufacturing systems is feasible and can provide rapid insights into such systems. In addition, the extracted models can be used to support decisions related to maintenance planning, parts procurement and system configuration.