Data-driven modelling, analysis and improvement of multistage production systems with predictive maintenance and product quality

Abstract

Predictive maintenance (PM) and quality management help to improve the business bottom line by alleviating the system performance degradation caused by unscheduled machine breakdown and product quality problems. In modern production systems, the wide application of new IT technology results in data-rich environments. However, it is not clear how to take advantage of the data to facilitate maintenance decision-making and production performance improvement. Aiming at multistage production systems with batching machines and finite buffers, this research studies data-driven modelling, analysis and improvement of production systems with predictive maintenance and product quality. First, a data-driven quantitative method is proposed to analyze the impact of machine breakdowns, predictive maintenance and product quality failure on system performance. Then, based on the obtained system production loss, a PM decision model is established to minimise the maintenance and production costs, and the optimal maintenance policy is exploited based on an approximate dynamic programming algorithm. In addition, downtime bottleneck (DT-BN) is defined, and a data-driven bottleneck indicator is derived. A continuous improvement method is established through the identification and mitigation of the bottlenecks. Finally, numerical case studies are performed to validate the effectiveness of the proposed PM decision model and continuous improvement method.