A change-point based reliability prediction model using field return data

Altun, Mustafa; Comert, Salih Vehbi

doi:10.1016/j.ress.2016.07.024

Cited by 15 publications

(6 citation statements)

References 32 publications

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“…Matthews and Farewell 1982;Loader 1991;Noura and Read 1990) are not appropriate in our situation where the hazard rates may rapidly decrease before the change point is reached. Maximum likelihood and pseudo maximum likelihood approaches using a Weibull model have been proposed in reliability theory to model the hazard rate for the time interval preceding the change point; see Kleyner and Sandborn (2005) and Altun and Comert (2016). These authors show that the parametric approach is useful for estimating the change point and for reliability prediction based on the respective model.…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly a comparable problem is encountered with specific products (e.g. automotive electronic), where di↵erent phases of hazard have been proposed when analyzing the time elapsed after sale and before warranty returns (see Kleyner and Sandborn 2005;Yang et al 2012;Altun and Comert 2016). The corresponding structures of hazard rates, and the importance of identifying change points, are comparable to those encountered in survival analyses of critically ill patients.…”

Section: Introductionmentioning

confidence: 98%

“…The case of a continuous hazard rate with a change point is on the other hand rarely covered. In a recent paper, Altun and Comert (2016) use Weibull-Exponential models to represent the typical L-shaped hazard rates of electronic products. While the latter authors make use of distributional assumptions, the first nonparametric approach with a Bayesian estimation strategy using a stochastic jump process model was proposed by Yang et al (2012).…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric change point estimation for survival distributions with a partially constant hazard rate

et al. 2018

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We present a new method for estimating a change point in the hazard function of a survival distribution assuming a constant hazard rate after the change point and a decreasing hazard rate before the change point. Our method is based on fitting a stump regression to p values for testing hazard rates in small time intervals. We present three real data examples describing survival patterns of severely ill patients, whose excess mortality rates are known to persist far beyond hospital discharge. For designing survival studies in these patients and for the definition of hospital performance metrics (e.g. mortality), it is essential to define adequate and objective end points. The reliable estimation of a change point will help researchers to identify such end points. By precisely knowing this change point, clinicians can distinguish between the acute phase with high hazard (time elapsed after admission and before the change point was reached), and the chronic phase (time elapsed after the change point) in which hazard is fairly constant. We show in an extensive simulation study that maximum likelihood estimation is not robust in this setting, and we evaluate our new estimation strategy including bootstrap confidence intervals and finite sample bias correction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Nonparametric change point estimation for survival distributions with a partially constant hazard rate

et al. 2018

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“…In 2016, Mustafa and Comert studied the change point of the reliability prediction model based on the field return data (Mustafa and Comert, 2016). Gao, Cui and Dong studied the reliability modeling of the single change point two-stage degradation system based on the Wiener process (Gao et al , 2020).…”

Section: Current Reliability Growth Modelsmentioning

confidence: 99%

On general uncertainty data and general uncertainty variable for reliability growth analysis of major aerospace equipment

Liu

Tang

2022

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PurposeThe purpose of this paper is to explore new ways and lay a solid foundation to solve the problem of reliability growth analysis of major aerospace equipment with various uncertainty data through propose new concepts of general uncertainty data (GUD) and general uncertainty variable (GUV) and build the operation system of GUVs.Design/methodology/approachThe characteristics of reliability growth data of major aerospace equipment and the limitations of current reliability growth models have been analyzed at first. The most commonly used uncertainty system analysis methods of probability statistics, fuzzy mathematics, grey system theory and rough set theory have been introduced. The concepts of GUD and GUV for reliability growth data analysis of major aerospace equipment are proposed. The simplified form of GUV based on the “kernel” and the degree of uncertainty of GUV is defined. Then an operation system of GUVs is built.Findings(1) The concept of GUD; (2) the concept of GUV; (3) The novel operation rules of GUVs with simplified form.Practical implicationsThe method exposed in this paper can be used to integrate complex reliability growth data of major aerospace equipment. The reliability growth models based on GUV can be built for reliability growth evaluation and forecasting of major aerospace equipment in practice. The reliability evaluation example of a solid rocket motor shows that the concept and idea proposed in this paper are feasible. The research of this paper opens up a new way for the analysis of complex uncertainty data of reliability growth of major aerospace equipment. Moreover, the operation of GUVs could be extended to the case of algebraic equation, differential equation and matrix which including GUVs.Originality/valueThe new concepts of GUD and GUV are given for the first time. The novel operation rules of GUVs with simplified form were constructed.

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“…Moreover, Yuan and Kuo [17] used the Weibull exponential distribution to simulate the L-shaped hazard rate function. A changepoint-based reliability prediction model using field return data was investigated by Altun and Comert to predict the reliability performance of high-volume complex electronic products during their warranty period [18]. Kleyne and Bender [19] introduced a practical method of reliability prediction that merges a military standard approach with manufacturer's warranty data to improve the accuracy of failure rate prediction.…”

Section: A Reliability Evaluationmentioning

confidence: 99%

Sub-Region Warranty Differential Pricing Optimization Strategy Based on Regional Granularity of Use Reliability

Jie

Liu

et al. 2020

IEEE Access

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Despite the various advantages of existing warranty designs, these designs are still limited to inherent reliability and ignore the interaction between use reliability and regional difference. A new sub-region warranty differential pricing strategy based on the regional granularity of use reliability is proposed to solve these issues. A use reliability prediction model based on regional granularity partition results and after-sales failure data is established to evaluate product use reliability accurately in different sub-regions. Then, a novel high-dimensional optimization model that considers the regional difference in use reliability, warranty, and price is developed to optimize the regional warranty differential pricing strategy. Two scenarios for pricing and warranty, namely, unified and partition warranty schemes, are considered, and the necessary optimality conditions for each scenario are determined. Afterward, a practical case study is conducted in the air-conditioning industry to verify the performance of the proposed model. The sensitivity of the model is also analyzed. Numerical experiments show that the sub-region warranty differential pricing strategy allows for a suitable trade-off among use reliability, warranty, and selling price. Moderately reducing the selling price of sub-regions with high use reliability and increasing the selling price of sub-regions with low use reliability can enhance profitability. This work provides manufacturers with guidelines on designing sub-region warranty differential pricing strategies.INDEX TERMS Sub-region warranty, differential pricing, use reliability, high-dimensional optimization model, air-conditioner.

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A change-point based reliability prediction model using field return data

Cited by 15 publications

References 32 publications

Nonparametric change point estimation for survival distributions with a partially constant hazard rate

Nonparametric change point estimation for survival distributions with a partially constant hazard rate

On general uncertainty data and general uncertainty variable for reliability growth analysis of major aerospace equipment

Sub-Region Warranty Differential Pricing Optimization Strategy Based on Regional Granularity of Use Reliability

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