A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction

Yaqiong Lv, Pan Zheng, Jiabei Yuan and Xiaohua Cao ()
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Yaqiong Lv: School of Transport and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Pan Zheng: School of Transport and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Jiabei Yuan: School of Transport and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Xiaohua Cao: School of Transport and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

Mathematics, 2023, vol. 11, issue 18, 1-23

Abstract: Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system.

Keywords: predictive maintenance; particle filtering; multi-component systems; system maintenance strategy; maintenance cost (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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