Economic allocation of reliability growth testing using Weibull distributions

Awad, Mahmoud

doi:10.1016/j.ress.2016.03.012

Cited by 30 publications

(13 citation statements)

References 24 publications

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“…Parameters α j , η j , and φ i describing machine performance state are fitted based on reliability test data or actual historical data, and detailed methods and procedures are available in Vlok et al 29 The values for parameters υ i and θ i in gamma processes are derived by means of maximum likelihood. 30 The value for parameter τ j is obtained by the sum of the products of the proportion of each failure mode and the corresponding repair time based on historical data, and the values for the parameters of the process model are derived based on the response surface method, 31 as displayed in Table 1 and equations (36)–(38)…”

Section: Case Studymentioning

confidence: 99%

Health prognosis approach for manufacturing systems based on quality state task network

Cui

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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Previous studies on health prognosis are exceedingly dependence on the failure data and sensor data of a single component of manufacturing systems, and the holistic health prognosis techniques applicable to whole manufacturing systems still remain a challenge due to its increasingly physical and functional complexity. Therefore, a generalized health prognosis method is presented based on the deep fusion of quality-oriented big data of operational process of manufacturing systems. First, the generalized connotation of manufacturing system health is explained from the aspects of the physical composition and functional characteristics of manufacturing systems, and the quality state task network is proposed to organize quality-oriented big operational data, which improve the state transparency of the manufacturing system and lay the foundation of holistic health prognosis. Second, key characterization parameters in quality state task network are defined. Specifically, the performance state is analyzed based on multistate characteristics by considering the effects of stochastic degradation processes; the product quality state is quantified by using a process model that is established based on monitoring and inspection data; and the task execution state is quantitatively described by analyzing the evolution of task demand among machines. Third, an integrated model is built by integrating the three above-mentioned states as two key indicators, namely, qualified degree and mission reliability, for the comprehensive prognosis of the health of manufacturing systems. Finally, the effectiveness of the proposed approach is verified with a case study on the health prognosis of a cylinder head manufacturing system.

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Section: Case Studymentioning

confidence: 99%

Health prognosis approach for manufacturing systems based on quality state task network

Cui

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…In particular, the Weibull distribution and log-normal distribution are widely used in reliability theoretical analysis. Though this has achieved certain results, they show large error and low precision in the process of product reliability estimation [10][11][12][13][14][15]. The accuracy of reliability models increasingly got the attention of scholars and experts.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Estimation on Reliability Models of Bearing Failure Data

Xia

Chang

Zhang

et al. 2018

Mathematical Problems in Engineering

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The failure data of bearing products is random and discrete and shows evident uncertainty. Is it accurate and reliable to use Weibull distribution to represent the failure model of product? The Weibull distribution, log-normal distribution, and an improved maximum entropy probability distribution were compared and analyzed to find an optimum and precise reliability analysis model. By utilizing computer simulation technology and -s hypothesis testing, the feasibility of three models was verified, and the reliability of different models obtained via practical bearing failure data was compared and analyzed. The research indicates that the reliability model of two-parameter Weibull distribution does not apply to all situations, and sometimes, two-parameter lognormal distribution model is more precise and feasible; compared to three-parameter log-normal distribution model, the threeparameter Weibull distribution manifests better accuracy but still does not apply to all cases, while the novel proposed model of improved maximum entropy probability distribution fits not only all kinds of known distributions but also poor information issues with unknown probability distribution, prior information, or trends, so it is an ideal reliability analysis model with least error at present.

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“…However, research on information integration in RGT is relatively scarce 28 and can be subjective. 29 To tackle the drawback of initial assumptions, Coit 20 and Awad 30 have concerned the limitation of uncertainty in their models of allocating testing times by considering the variability of failure intensity rate estimate. Heydari and Sullivan 31 studied the robust allocation problem of testing resources in reliability growth; the AMSAA/Crow parameters in their model are uncertain but assumed to lie within a budget-restricted uncertainty set.…”

Section: Introductionmentioning

confidence: 99%

“…Heydari et al 34 considered the connection-induced failures and modeled the optimal allocation of component-, subsystem- and system-level testing resources in the reliability growth of a series–parallel system. Awad 30 proposed a new method to allocate RGT time for both the subsystem and system levels in order to minimize system failure intensity under limited cost and time resources. Wilson and Quigley 35 investigated a Bayesian approach to the allocation of reliability tasks during product development, and they have developed a general multi-attribute utility function based on targets for cost, time on test and system reliability.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of test times for reliability growth testing with interval-valued model parameters

Wang

Hou

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Reliability growth testing is widely used to identify and remove failure modes in the development of complex systems. Different models have been proposed to track the progress of reliability growth during test, and previous research has addressed the improvement of after-testing system reliability by allocating limited testing resources. The majority of reliability growth testing models are based on the AMSAA/Crow model with known parameters, but there is a lack of work focusing on the situation when the AMSAA/Crow parameters are subject to uncertainty. In this article, we investigate a reliability growth testing allocation problem to series–parallel systems that considers parameter uncertainty in the AMSAA/Crow models. The model parameters are assumed to be known as uncertain-but-bounded values. Interval arithmetic and an interval order relation reflecting decision maker’s preference are used to analyze the uncertain parameters. In order to determine the optimal allocation of testing time for each component or subsystem aiming to maximize the after-testing system reliability, a modified genetic algorithm is developed. A penalty function is used to handle the testing resource limitations, and the techniques of dual mutation and random keys are used in the algorithm to improve searching efficiency. Computational results indicate that the interval analysis is a useful tool to deal with parameter uncertainty of reliability growth testing allocation problems.

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Economic allocation of reliability growth testing using Weibull distributions

Cited by 30 publications

References 24 publications

Health prognosis approach for manufacturing systems based on quality state task network

Health prognosis approach for manufacturing systems based on quality state task network

Estimation on Reliability Models of Bearing Failure Data

Optimal allocation of test times for reliability growth testing with interval-valued model parameters

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