Lifetime Estimation Using Only Failure Information From Warranty Database

Alam, Md. Mesbahul; Suzuki, Kazuyuki

doi:10.1109/tr.2009.2026788

Cited by 27 publications

(18 citation statements)

References 17 publications

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“…The above two points (a) and (b) can also be observed in those warranty data illustrated in Figure 2 of Alam and Suzuki [19] and in Figure 2 of Rai and Singh [22].…”

Section: Modelling Two-dimensional Reliability Datasupporting

confidence: 66%

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Construction of asymmetric copulas and its application in two-dimensional reliability modelling

2014

European Journal of Operational Research

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Copulas offer a useful tool in modelling the dependence among random variables. In the literature, most of the existing copulas are symmetric while data collected from the real world may exhibit asymmetric nature. This necessitates developing asymmetric copulas that can model such data. In the meantime, existing methods of modelling two-dimensional reliability data are not able to capture the tail dependence that exists between the pair of age and usage, which are the two dimensions designated to describe product life. This paper proposes a new method of constructing asymmetric copulas, discusses the properties of the new copulas, and applies the method to fit two-dimensional reliability data that are collected from the real world.

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“…The above two points (a) and (b) can also be observed in those warranty data illustrated in Figure 2 of Alam and Suzuki [19] and in Figure 2 of Rai and Singh [22].…”

Section: Modelling Two-dimensional Reliability Datasupporting

confidence: 66%

“…It indirectly estimates F (x 1 , x 2 ) through the following two steps: to estimate F 2 (x 2 |x 1 ) and F 1 (x 1 ), separately, then to obtain F (x 1 , x 2 ) = F 2 (x 2 |x 1 )F 1 (x 1 ) (where x 1 and x 2 represent age and usage, respectively). This method treats the usage as a function of the age [18,19].…”

Section: Modelling Two-dimensional Reliability Datamentioning

confidence: 99%

Construction of asymmetric copulas and its application in two-dimensional reliability modelling

2014

European Journal of Operational Research

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“…Under a recognized definition of lifetime, that is, first hitting time (FHT) [6,7], many RUL estimation approaches have been reported in literature [8][9][10]. References [6,11] provide a detailed overview of the degradation modeling and RUL predicting method.…”

Section: Introductionmentioning

confidence: 99%

“…References [6,11] provide a detailed overview of the degradation modeling and RUL predicting method. It can be founded that these models mainly concern the system degrading with a soothing wave.…”

Section: Introductionmentioning

confidence: 99%

Estimating Remaining Useful Life for Degrading Systems with Large Fluctuations

Zhang

Zhou

et al. 2018

Journal of Control Science and Engineering

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Remaining useful life (RUL) prediction method based on degradation trajectory has been one of the most important parts in prognostics and health management (PHM). In the conventional model, the degradation data are usually used for degradation modeling directly. In engineering practice, the degradation of many systems presents a volatile situation, that is, fluctuation. In fact, the volatility of degradation data shows the stability of system, so it could be used to reflect the performance of system. As such, this paper proposes a new degradation model for RUL estimation based on the volatility of degradation data. Firstly the degradation data are decomposed into trend items and random items, which are defined as a stochastic process. Then the standard deviation of the stochastic process is defined as another performance variable because standard deviation reflects the system performance. Finally the Wiener process and the normal stochastic process are used to model the trend items and random items separately, and then the probability density function (PDF) of the RUL is obtained via a redefined failure threshold function that combines the trend items and the standard deviation of the random items. Two practical case studies demonstrate that, compared with traditional approaches, the proposed model can deal with the degradation data with many fluctuations better and can get a more reasonable result which is convenient for maintenance decision.

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“…As suggested in those works, inference based on warranty databases has received increasing attention over the past 25 years (cf. Suzuki (1985ab), Kalbfleisch, Lawless and Robinson (1991), Lawless, Hu and Cao (1995), Lawless (1988a,1996), Lawless (1996ab, 1997), Wu and Meeker (2002), Alam and Suzuki (2009)); see Lawless (1998) and Karim and Suzuki (2005) for further references.…”

Section: Introductionmentioning

confidence: 99%

A pseudo-likelihood analysis for incomplete warranty data with a time usage rate variable and production counts

2013

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Abstract:The most direct purpose of collecting warranty data is tracking associated costs. But they are also useful for quantifying a relationship between use rate and product time-to-first-failure and for estimating the distribution of product time-to-first-failure (which we model as depending upon use rate and a unit potential life length under continuous use). Employing warranty data for such reliability analysis purposes is typically complicated by the fact that some parts of some warranty data records are missing. We introduce pseudo-likelihood methodology for dealing with some kinds of incomplete warranty data (like those available in a motivating real case from a machine manufacturer). Based on this, we estimate a use rate distribution, the distribution of time to first failure, and the time associated with a cumulative probability of first failure.

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Lifetime Estimation Using Only Failure Information From Warranty Database

Cited by 27 publications

References 17 publications

Construction of asymmetric copulas and its application in two-dimensional reliability modelling

Construction of asymmetric copulas and its application in two-dimensional reliability modelling

Estimating Remaining Useful Life for Degrading Systems with Large Fluctuations

A pseudo-likelihood analysis for incomplete warranty data with a time usage rate variable and production counts

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