Optimal Design for Destructive Degradation Tests With Random Initial Degradation Values Using the Wiener Process

Xiao, Xun; Ye, Zhi‐Sheng

doi:10.1109/tr.2016.2575442

Cited by 45 publications

(17 citation statements)

References 29 publications

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“…For a better illustration of the proposed model without loss of generality, and following classical assumptions in relevant papers [9,16,17,20,23,30], the rest of this article will respectively take…”

Section:  mentioning

confidence: 99%

“…First of all, a random effect model [16] is often adapted to capture the conventional unit-to-unit variability referring to the heterogeneity among degradation rates of units. Besides that, the variability in initial degradation values [17] of different units also leads to diversity in reliability analysis of such degradation processes. Furthermore, the traditional SSADTs always elevate the test stress to a higher level at some predetermined time-points for all units, which causes a problem that some units may experience little or even no degradation within a specified test duration.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear step-stress accelerated degradation modelling considering three sources of variability

Hao

Yang

Bérenguer

2018

Reliability Engineering & System Safety

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In the absence of enough run-to-failure data, step-stress accelerated degradation testing (SSADT) is often an attractive alternative way to evaluate the reliability of a product, with the advantage of requiring small sample size and short test time. However, the development of a statistical SSADT model for reliability assessment should take into account different sources of variability in the degradation process that generate uncertainty: 1) temporal variability determining the inherent variability of degradation process over time; 2) unit-to-unit variability in three aspects: degradation rates, initial degradation values, time-points of elevating stress levels; and 3) measurement errors in both covariates and degradation performance. As a contribution towards this aim, a new nonlinear Wiener-process-based SSADT model considering simultaneously nonlinearity and three sources of variability is proposed. Using the proposed SSADT model, the lifetime law of the tested product under normal conditions is derived based on the concept of first hitting time (FHT) of a predetermined failure threshold. Following an approach based on genetic algorithms (GA), a modified simulation and extrapolation method, called GA-SIMEX, is also developed for the model parameter estimation. Finally, a simulation study of fatigue crack length growth is presented to illustrate the implementation of the proposed SSADT model.

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Section:  mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear step-stress accelerated degradation modelling considering three sources of variability

Hao

Yang

Bérenguer

2018

Reliability Engineering & System Safety

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“…With the help of theory in [7], when X(0)=z0 ≥ l, the product failed at time 0. The probability can be expressed as…”

Section: Life Predictionmentioning

confidence: 99%

“…In this paper, we assume that the initial value is a random variable which obey normal distribution and build a performance degradation model. Then we perform reliability analysis referred to the reliability model of Xun Xiao et al [7] . In this way, fuel servo-valve's life can be effectively predicted.…”

Section: Introductionmentioning

confidence: 99%

Reliability Evaluation of Fuel Servo-valve With Random Initial Degradation Values

Fan¹,

Ma²

2018

dtetr

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*The fuel servo-valve is a critical component of servo control system. So its reliability greatly affects the reliability of the entire engine system. So how to estimate the storage reliability of fuel servo-valve using degradation data is an urgent problem to be solved. Noticed that the initial value of the degradation data with high dispersity is not 0. We first establish the Wiener process model with random initial value. This initial value obeys normal distribution. Next, we estimate the model parameters by means of MLE (maximum likelihood estimation). In the end, storage reliability estimation was done in the light of certain batch of fuel servo-valve.

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“…Hidden failure itself does not lead to system breakdown, but the existence of hidden failure may cause performance loss during system operation. Hidden failure is very common in modern systems, particularly in stand-by redundant systems and protection systems (Xiao & Ye, 2016). Yang et al (2006) reported that hidden failure in protection system may cause multiple component outages and even spread power system disturbances.…”

Section: Introductionmentioning

confidence: 99%

Maintenance Scheduling for Multicomponent Systems with Hidden Failures

et al. 2017

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This paper develops a maintenance policy for a multi-component system subject to hidden failures. Components of the system are assumed to suffer from hidden failures, which can only be detected at inspection. The objective of the maintenance policy is to determine the inspection intervals for each component such that the long-run cost rate is minimized. Due to the dependence among components, an exact optimal solution is difficult to obtain. Concerned with the intractability of the problem, a heuristic method named "base interval approach" is adopted to reduce the computational complexity. Performance of the base interval approach is analyzed and the result shows that the proposed policy can approximate the optimal policy within a small factor. Two numerical examples are presented to illustrate the effectiveness of the policy.

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Optimal Design for Destructive Degradation Tests With Random Initial Degradation Values Using the Wiener Process

Cited by 45 publications

References 29 publications

Nonlinear step-stress accelerated degradation modelling considering three sources of variability

Nonlinear step-stress accelerated degradation modelling considering three sources of variability

Reliability Evaluation of Fuel Servo-valve With Random Initial Degradation Values

Maintenance Scheduling for Multicomponent Systems with Hidden Failures

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