Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

Xiao, Hui; Cao, Minhao; Kou, Gang; Yuan, Xiaojun

doi:10.1177/1748006x20947846

Cited by 9 publications

(5 citation statements)

References 50 publications

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“…[1][2][3] It is very important for reliability allocation whether the reliability index can be reasonably and effectively allocated to each subsystem. [4][5] All the researches on the reliability of CNC machine tools are based on the hypothesis that failure independence and reliability factors are independent of each other. However, a large number of field production data show that the independent hypothesis cannot accurately describe the actual operating state of CNC machine tools, and the established reliability allocation model has a certain deviation.…”

Section: Introductionmentioning

confidence: 99%

“…As the “industrial master machine” in the manufacturing industry, the reliability design of CNC machine tools has a direct impact on the cost of manufacturing and the Mean Time Between Failures (MTBF) in operation 1–3 . It is very important for reliability allocation whether the reliability index can be reasonably and effectively allocated to each subsystem 4–5 . All the researches on the reliability of CNC machine tools are based on the hypothesis that failure independence and reliability factors are independent of each other.…”

Section: Introductionmentioning

confidence: 99%

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Reliability allocation method of comprehensive weight computer numerical control machine tool based on failure correlation and factor correlation

Yuhong

et al. 2022

Quality & Reliability Eng

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According to the shortage of independent hypothesis allocation models between influence factors and subsystems, a reliability allocation method considering influence factor correlation and subsystem failure correlation is proposed. The CNC (Computer Numerical Control) machine tool is taken as the research object. Using Copula function to analyze failure correlation analysis between subsystems, establish subsystem complete reliability model and integrated importance measure model under failure correlation. The four factors of influencing reliability allocation are subsystem complexity, integrated importance measure of subsystem based on failure correlation, failure frequency, and specific gravity ratio of failure downtime. The correlation analysis among the influencing factors was carried out by using gray correlation theory, and the subjective weight of the subsystem was determined. The IEW‐VIKOR (Information Entropy Weight‐Vlsekriterijumska Optimizacija I Kompromisno Resenje) method is used to determine the objective weights of subsystems. The comprehensive weight of the subsystem is also obtained through combinatorial empowerment. The reliability allocation of the CNC machine tool subsystem was carried out according to the failure correlation reliability model, and the results of failure independence and failure correlation hypothesis allocation were compared and analyzed. When the reliability index is the same, the reliability allocation method proposed in this paper can assign lower reliability to each subsystem, which is of great significance to improve the reliability of CNC machine tools and reduce the difficulty of reliability design

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability allocation method of comprehensive weight computer numerical control machine tool based on failure correlation and factor correlation

Yuhong

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…In recent years, the reliability of multi-state systems has been studied from different points of view by many authors; the reader is referred. [1][2][3][4][5][6] This study is an attempt to investigate the reliability of a three-state system consisting of n binary components. Under the condition that the components of the system are from different types, we investigate the joint reliability function of the state lifetimes of the system.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling of three-state systems with multiple types of components

Ashrafi

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this paper, a system consisting of three states: perfect functioning, partial functioning, and down is considered. The system is assumed to be composed of several non-identical groups of binary components. The reliability of the system states under various assumptions on the component lifetimes is investigated. For this purpose, first, a new concept of bivariate survival signature (BSS) is introduced. Then, under the assumption that the component lifetimes of each type are exchangeable dependent, representations for the joint reliability function of the state lifetimes are obtained based on the notion of BSS. In the particular case, three-state systems composed of two types of different modules such as general-series (parallel) systems and systems with component-wise redundancy are investigated. Several examples are presented to illustrate the theoretical results.

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“…To derive the probabilistic indices of the reliability models based on practical engineering cases, existing research has put forward various methodologies of reliability analysis, including universal generating function technique 29,30 and stochastic process models [31][32][33] and so on. Owing to its high efficiency and effectiveness in reliability analyses, a multi-stage FMCIA is formulated for the reliability assessment of the proposed shock model.…”

Section: Introductionmentioning

confidence: 99%

Generalized mixed shock model for multi-component systems in the shock environment with a change point

Wang

Ning

Zhao

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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The reliability of various systems under shock models has been explored extensively in the literature, where the constant mechanism of shock impact has always been studied. Nevertheless, engineering systems operate in a more complicated shock environment due to developments, and it is worth studying the reliability of such multi-component systems in a variable shock environment. Driven by the research gaps and practical situations, this paper constructs a reliability model of multi-component systems under a generalized mixed shock model with a change point. The multi-state components operate in the shock environment I initially and it may continue to run in the shock environment II after the random change point of the environment. Two novel shock impact mechanisms of the variable environment are put forward, where a series of failure criteria based on shocks are included. Four different structures of multi-component systems are considered in this paper. It can be proved that the proposed mixed shock model is a generalization of some transformed models. A multi-stage finite Markov chain imbedding approach is established to derive the probabilistic indices of the components and entire systems. Based on the engineering applications, illustrative examples are provided to verify the effectiveness of the proposed model.

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Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

Cited by 9 publications

References 50 publications

Reliability allocation method of comprehensive weight computer numerical control machine tool based on failure correlation and factor correlation

Reliability allocation method of comprehensive weight computer numerical control machine tool based on failure correlation and factor correlation

Reliability modeling of three-state systems with multiple types of components

Generalized mixed shock model for multi-component systems in the shock environment with a change point

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