Mission Reliability-Oriented Selective Maintenance Optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

Chen, Zhaoxiang; He, Yihai; Zhao, Yuan; Han, Xiao; Liu, Fengdi; Zhou, Di; Wang, Wenzhuo

doi:10.1109/access.2019.2933580

Cited by 20 publications

(9 citation statements)

References 33 publications

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“…(2) The dynamic degradation modeling of equipment should be further studied and applied, such as the dynamic updating method of the model parameters in maintenance decision-making [23], [24], [26] and the dynamic simulation method [29] of equipment operationmaintenance process. 3The maintenance strategy should be further studied in the aspect of mission-orientation [30], [43], and be applied to more fields, such as intelligent manufacturing system [44].…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process

Guo

Axita

et al. 2020

IEEE Access

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Condition-based maintenance has been widely used in the maintenance strategy of equipment or systems. Aiming at the maintenance decision-making of the equipment with dynamic degradation characteristics, a dynamic condition-based maintenance model is proposed based on Inverse Gaussian process in this paper. Firstly, an Inverse Gaussian process with stochastic parameter is proposed to describe the change of the equipment degradation characteristics during operation, and the important stochastic properties related to condition-based maintenance is deduced. Secondly, the dynamic maintenance threshold function is proposed, and its different values at different degradation stages can reduce the early failure risk of the equipment while ensuring a lower expected cost ratio. On this basis, a dynamic maintenance decision-making model with multi-objectives is established. A numerical example is illustrated to verify the correctness and practicability of the proposed method, and the sensitivity analysis results of the related parameters prove the necessity of considering the dynamic degradation characteristics of equipment. The comparison result proves that the method proposed in this paper can obtain better safety and economy of maintenance than the method with fixed thresholds. INDEX TERMS Condition-based maintenance, dynamic degradation characteristics, inverse Gaussian process, dynamic maintenance threshold function, dynamic maintenance.

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Section: Discussionmentioning

confidence: 99%

A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process

Guo

Axita

et al. 2020

IEEE Access

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“…1) Determine the minimum processing load of each machine based on Eqs. (7)(8)(9) 2) For each machine, determine the minimum capacity requirements S ∂(j,i) to satisfy the processing load. According to Eq.…”

Section: B Illustrated Examplementioning

confidence: 99%

“…A. DERIVATION OF EQUATION 8, (9) As shown in Figure 4, B I 1 i in-process products output ρ 1 i B I 1 i qualified products, and B O2 i = ρ 2 i B I 1 i defective products are generated. After reprocessing, ρ 1 i B O2 i qualified products are output.…”

Section: Appendixmentioning

confidence: 99%

“…An intelligent manufacturing system is a typical multi-state system [9], [10]. Due to the complex function and structure of manufacturing system, the binary state reliability theory based on the criteria of normal function and fault cannot effectively reflect a system's actual state, nor can it satisfy the reliability evaluation requirements of a complex manufacturing system.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Modeling Method of Mission Reliability of a Multi-State Manufacturing System With Multiple Production Lines

Zhang

Wang

et al. 2020

IEEE Access

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Mission reliability could systematically characterize the running state of a manufacturing system and the rationality of production task setting in advance. However, few studies can realize the mission reliability modeling, which integrates production scheduling based on fully considering the complexity, dynamics, and multi-state characteristics of a manufacturing system. Thus, a dynamic method for modeling the mission reliability of a multi-state manufacturing system with multiple production lines is proposed in this paper. First, the multi-state characteristic of manufacturing systems operation is analyzed. On this basis, considering the composition of manufacturing systems, the connotation and modeling mechanism of the mission reliability of manufacturing systems are proposed. Second, combining the discretization of machine performance states and dynamic continuity of the performance degradation process, the dynamic modeling method of multi-state machine performance and buffer usage are respectively discussed. The transfer evolution of task demands in the production line is analyzed, and the decomposition method of task scheduling and load mapping in multiple production lines is provided. Third, considering the characteristics of multiple production lines and the buffer of a manufacturing system, taking production tasks as the core and integrating production scheduling, a modeling method for the mission reliability of manufacturing systems is proposed. Finally, the effectiveness of the proposed approach is verified using a case study on the modeling of the mission reliability of a manufacturing system of aerospace parts.

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“…In Reference [20], the RCM is applied to the whole system under test instead of focusing on individual components. Other papers use analytical models and a dynamic approach [21,22], while some authors create their own framework for maintenance decisionmaking [23,24]. Zakikhani et al [25] propose an availability-based RCM, while in [26] a whole dependability study (RAMS) is introduced to optimize maintenance policy.…”

Section: Introductionmentioning

confidence: 99%

Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection

et al. 2021

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Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method.

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Mission Reliability-Oriented Selective Maintenance Optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

Cited by 20 publications

References 33 publications

A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process

A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process

Dynamic Modeling Method of Mission Reliability of a Multi-State Manufacturing System With Multiple Production Lines

Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection

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