Component-Targeting Opportunistic Maintenance Policy for Multi-Machine System Considering Hierarchical Structural Dependencies

Increasing market competition and growing mechanical structural complexity have urged the application of complex systems comprising multi-component machines. Ensuring the economical operation and maintenance (O&M) for such systems is paramount in industry. To achieve this, the component maintenance policy should cover both the machine-level disassembly sequence among critical components and the system-level configuration setting connecting individual machines. However, integrating such hierarchical structural dependencies into maintenance decision-making poses a formidable challenge and remains unexplored in existing literature. Facing this key issue, our study presents a component-targeting opportunistic maintenance policy considering hierarchical structural dependencies (COMP-HSD) to provide real-time cost-effective maintenance schemes. Aiming at deriving the optimal component maintenance group at each maintenance opportunity, this policy builds a decision-making process including triple-level analyzing framework to comprehensively involve the component degradation and the structural dependencies analyses of machine level and system level. Specifically, sequential component-level predictive maintenance (PdM) intervals are initially optimized according to individual health conditions. Then, a maintenance cost savings model is developed to assess the economy of adjusting PdM actions for each component at the current maintenance opportunity, accounting for the impacts of hierarchical structural dependencies. Furthermore, we incorporate maintenance resource limitation into the decision-making procedure to enhance the adaptability to industrial practice. Numerical examples extracted from a partner engine cylinder head manufacturing enterprise certify the significant cost reduction of this COMP-HSD methodology.Note to Practitioners—This work is motivated by the critical problem of calculating a cost-effective component-targeting maintenance policy for a complex production system with multi-component machines. According to the real O&M management of a manufacturing company, it is intractable to reasonably decide when to maintain each critical component in multiple CNC machine tools of a production system, which restricts the maintenance planning economy. This is due to the difficulty of evaluating the impacts brought by hierarchical structural dependencies (i.e., machine-level disassembly sequence and system-level series configuration). Existing approaches are limited to be directly applied to practice since they either focus solely on individual multi-component machine or assume that each machine in a multi-machine system only contains one component, neglecting the holistic evaluation. Besides, the maintenance resource restriction is rarely considered in the literature. To fill this gap, we present the COMP-HSD policy, which simultaneously considers the individual component degradations, systemic maintenance opportunities and complex structural dependencies analysis in the maintenance decision-making process. Referring to this policy, the manufacturers could determine the component maintenance timetable to conduct timely PdM actions with lower cost. Compared with the traditional maintenance policy, our study greatly reduces the total cost, and also offers extending applications such as maintenance tool preparation and spare part ordering according to the component maintenance scheme.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2024-11-28
DOI
https://doi.org/10.1109/tase.2024.3504723
Citations
4
Primary Topic
Software Reliability and Analysis Research
Type
article
Field-Weighted Citation Impact
1.94

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Component-Targeting Opportunistic Maintenance Policy for Multi-Machine System Considering Hierarchical Structural Dependencies

Yutong Ding, Ershun Pan, Meimei Zheng, Lifeng Xi et al.
4 citations
IEEE Transactions on Automation Science and Engineering
Software Reliability and Analysis Research
1.94
article

Component-Targeting Opportunistic Maintenance Policy for Multi-Machine System Considering Hierarchical Structural Dependencies

Yutong Ding, Ershun Pan, Meimei Zheng, Lifeng Xi, Kaigan Zhang, Tangbin Xia, Dong Wang
article en
4 citations

Abstract

Increasing market competition and growing mechanical structural complexity have urged the application of complex systems comprising multi-component machines. Ensuring the economical operation and maintenance (O&M) for such systems is paramount in industry. To achieve this, the component maintenance policy should cover both the machine-level disassembly sequence among critical components and the system-level configuration setting connecting individual machines. However, integrating such hierarchical structural dependencies into maintenance decision-making poses a formidable challenge and remains unexplored in existing literature. Facing this key issue, our study presents a component-targeting opportunistic maintenance policy considering hierarchical structural dependencies (COMP-HSD) to provide real-time cost-effective maintenance schemes. Aiming at deriving the optimal component maintenance group at each maintenance opportunity, this policy builds a decision-making process including triple-level analyzing framework to comprehensively involve the component degradation and the structural dependencies analyses of machine level and system level. Specifically, sequential component-level predictive maintenance (PdM) intervals are initially optimized according to individual health conditions. Then, a maintenance cost savings model is developed to assess the economy of adjusting PdM actions for each component at the current maintenance opportunity, accounting for the impacts of hierarchical structural dependencies. Furthermore, we incorporate maintenance resource limitation into the decision-making procedure to enhance the adaptability to industrial practice. Numerical examples extracted from a partner engine cylinder head manufacturing enterprise certify the significant cost reduction of this COMP-HSD methodology.Note to Practitioners—This work is motivated by the critical problem of calculating a cost-effective component-targeting maintenance policy for a complex production system with multi-component machines. According to the real O&M management of a manufacturing company, it is intractable to reasonably decide when to maintain each critical component in multiple CNC machine tools of a production system, which restricts the maintenance planning economy. This is due to the difficulty of evaluating the impacts brought by hierarchical structural dependencies (i.e., machine-level disassembly sequence and system-level series configuration). Existing approaches are limited to be directly applied to practice since they either focus solely on individual multi-component machine or assume that each machine in a multi-machine system only contains one component, neglecting the holistic evaluation. Besides, the maintenance resource restriction is rarely considered in the literature. To fill this gap, we present the COMP-HSD policy, which simultaneously considers the individual component degradations, systemic maintenance opportunities and complex structural dependencies analysis in the maintenance decision-making process. Referring to this policy, the manufacturers could determine the component maintenance timetable to conduct timely PdM actions with lower cost. Compared with the traditional maintenance policy, our study greatly reduces the total cost, and also offers extending applications such as maintenance tool preparation and spare part ordering according to the component maintenance scheme.

IEEE Transactions on Automation Science and EngineeringVol. 22
Shanghai Jiao Tong University (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 11%
Software Reliability and Analysis Research
1.94
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.