A collaborative model for predictive maintenance of after-sales equipment based on digital twin

作者：Xiao Li, Hongfei Liang, Yu‐Chen Chen, Yuanpeng Ruan, Lei Wang · 发表于：European J of Industrial Engineering · 年份：2023 · DOI：10.1504/ejie.2023.133174 · 被引用次数：1 · 研究领域：Engineering Diagnostics and Reliability、Technology Assessment and Management、Digital Transformation in Industry

In response to the demands of users for prompting fault diagnosis and maintenance, equipment manufacturers require more advanced maintenance technologies for real-time monitoring, prediction, and remote guidance. Based on digital twin, this paper puts forward a seven-dimensional model of collaborative maintenance and a collaborative model for after sales maintenance service, which enables manufacturers to provide more effective and timely service and support to their customers. Taking a bottled water capping process as an example, it constructs a digital twin-driven model for predicting the remaining effective life of devices, a digital twin service platform with a maintenance knowledge database. Based on the forward variable combining the current state and state duration from hidden semi-Markov chain, and the improved formula for calculating the remaining effective life of equipment state, the feasibility of the proposed seven-dimensional collaborative maintenance model and the collaborative model for after sales maintenance service are verified. [Submitted: 20 July 2021; Accepted: 8 August 2022]