Data-Driven Maintenance Priority and Resilience Evaluation of Performance Loss in a Main Coolant System

Dui, Hongyan; Xu, Zhe; Chen, Liwei; Xing, Liudong; Liu, Bin

doi:10.3390/math10040563

Cited by 26 publications

(6 citation statements)

References 30 publications

(27 reference statements)

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“…. , 21), where O i denotes the probability of failure of path i. Therefore, the reliability function of the system is…”

Section: ) Hot Standby Gatesmentioning

confidence: 99%

“…As can be seen from the above analysis, both static and dynamic fault trees make some assumptions about the fault state of the system. These include the assumption that the event has only two states, "normal" and "fault", without considering the existence of other intermediate states [21]. As a result, there is a problem of inaccurate description of the system state and unclear identification of the system failure mode [22], and any errors can lead to large-scale economic losses [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Reliability and Service Life Analysis of Airbag Systems

2023

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Airbag systems are important to a car’s safety protection system. To further improve the reliability of the system, this paper analyzes the failure mechanism of automotive airbag systems and establishes a dynamic fault tree model. The dynamic fault tree model is transformed into a continuous-time Bayesian network by introducing a unit step function and an impulse function, from which the failure probability of the system is calculated. Finally, the system reliability and average life are calculated and analyzed and compared with the sequential binary decision diagram method. The results show that the method can obtain more accurate system reliability and effectively identify the weak parts of the automotive airbag system, to a certain extent compensating for the lack of computational complexity of dynamic Bayesian networks in solving system reliability problems with continuous failure processes.

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“…. , 21), where O i denotes the probability of failure of path i. Therefore, the reliability function of the system is…”

Section: ) Hot Standby Gatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability and Service Life Analysis of Airbag Systems

2023

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“…Dui et al [27] proposed resilience importance based on performance loss to evaluate the performance recovery efficiency of a main coolant system. Zhang et al [28] proposed the elastic efficiency importance measure and maintenance efficiency measure to determine the importance of components and guide the maintenance sequence.…”

Section: Literature Reviewmentioning

confidence: 99%

Redundancy-Based Resilience Optimization of Multi-Component Systems

2023

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Systems are damaged due to various disturbances, and the reliability of the systems is reduced. Measures to improve system resilience need to be studied since many systems still need to operate normally after suffering damage. In this paper, the whole process of the disturbance and recovery of the system is considered, and a resilience optimization model of a multi-component system is proposed. Firstly, a system resilience assessment method is proposed based on system reliability, and the system resilience loss is used as the resilience assessment index. Secondly, two component importance indexes, loss importance and recovery importance, are proposed for the system disturbance phase and recovery phase, respectively. The two importance indexes are weighted to obtain the weighted importance so as to measure the change law of system resilience and determine the influence degrees of components on system reliability. Then, under the constraint of maintenance time, an optimization model is established to determine a redundancy strategy to maximize system resilience. Finally, through an example analysis of a wind turbine system with its main components, it is verified that the redundancy strategy proposed with this method can reduce the loss of system resilience and effectively improve system reliability.

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“…Condition-based maintenance and its newest evolution, predictive maintenance, have attracted a lot of attention in aviation equipment maintenance with the development of sensor-monitoring and live forecast technology [1,4]. Compared to age-based maintenance, its greatest benefit is that maintenance can be carried out according to the actual operation state of the equipment to avoid extra downtime losses caused by incorrect judgment based on prior information or a lack of understanding of the equipment health status [5,6]. Reliability analyses, as well as maintenance optimization approaches, in regard to performance deterioration have been widely reviewed in the past several years.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Scheduling of Intelligent Group Maintenance Planning under Usage Availability Constraint

Chen

Wei

et al. 2022

Mathematics

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Maintenance, particularly preventive maintenance, is a crucial measure to ensure the operational reliability, availability, and profitability of complex industrial systems such as nuclear asset, wind turbines, railway trains, etc. Powered by the continuous advancement of sensor technology, condition-based group maintenance has become available to enhance the execution efficiency and accuracy of maintenance plans. The majority of existing group maintenance plans are static, which require the prescheduling of maintenance sequences within fixed windows and, thus, cannot fully utilize real-time health information to ensure decision-making responsiveness. To address this problem, this paper proposes an intelligent group maintenance framework that is capable of dynamically and iteratively updating all component health information. A two-stage analytical maintenance model was formulated to capture the comprehensive impact of scheduled maintenance and opportunistic maintenance through failure analyses of both degradation and lifetime components. The penalty functions for advancing or postponing maintenance were calculated based on the real-time state and age information of each component in arbitrary groups, and the subsequent grouping of the time and sequence of components to be repaired were iteratively updated. A lifetime maintenance cost model was formulated and optimized under a usage availability constraint through the sequential dynamic programming of group sequences. Numerical experiments demonstrated the superior performance of the proposed approach in cost control and availability insurance compared with conventional static and periodic maintenance approaches.

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Data-Driven Maintenance Priority and Resilience Evaluation of Performance Loss in a Main Coolant System

Cited by 26 publications

References 30 publications

Reliability and Service Life Analysis of Airbag Systems

Reliability and Service Life Analysis of Airbag Systems

Redundancy-Based Resilience Optimization of Multi-Component Systems

Dynamic Scheduling of Intelligent Group Maintenance Planning under Usage Availability Constraint

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