Parameter Estimation Methods for Condition-Based Maintenance With Indirect Observations

Ghasemi, Alireza; Yacout, Soumaya; Ouali, M-Salah

doi:10.1109/tr.2010.2048736

Cited by 53 publications

(36 citation statements)

References 17 publications

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“…This approach uses stochastic models that combine three main sources of information: Work [25] contains methods to estimate the parameters of condition monitored equipment whose failure rate follows the Cox's time-dependent Proportional Hazards model. The errors of measurements, instrumentation inaccuracy, interpretation mistakes, and imperfect observations cause misinterpretation in a degradation state.…”

Section: State-of-the-art/literature Survey On Failure Occurrencementioning

confidence: 99%

Failure prediction of diesel engine based on occurrence of selected wear particles in oil

Vališ

Žák

Pokora

2015

Engineering Failure Analysis

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Section: State-of-the-art/literature Survey On Failure Occurrencementioning

confidence: 99%

Failure prediction of diesel engine based on occurrence of selected wear particles in oil

Vališ

Žák

Pokora

2015

Engineering Failure Analysis

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“…The steps for evaluating SF and MRL are summarized as: À obtain t 0 ; D m ; ZðD m Þ; T i ; i\ZðD m Þ and determine g xjx j according to historical inspection data;`evaluate the survival function by (9), (11) and (13);´evaluate the mean residual life by (15), (16) and (17).…”

Section: ) a New Componentmentioning

confidence: 99%

“…The same model is adopted to identify the optimal inspection period and the replacement policy [15]. The parameter estimation problem is studied in [16]. A control-limit policy and parameter estimation are proposed in [17] where an optimal replacement policy is obtained to minimize the average replacement costs per unit time.…”

Section: Introductionmentioning

confidence: 99%

Applications of survival functions to continuous semi-Markov processes for measuring reliability of power transformers

Wang

Shahidehpour

Guo

2017

J. Mod. Power Syst. Clean Energy

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The reliability of power transformers is subject to service age and health condition. This paper proposes a practical model for the evaluation of two reliability indices: survival function (SF) and mean residual life (MRL). In the proposed model, the periodical modeling of power transformers are considered for collecting the information on health conditions. The corresponding health condition is assumed to follow a continuous semi-Markov process for representing a state transition. The proportional hazard model (PHM) is introduced to incorporate service age and health condition into hazard rate. In addition, the proposed model derives the analytical formulas for and offers the analytical evaluation of SF and MRL. SF and MRL are calculated for new components and old components, respectively. In both cases, the proposed model offers rational results which are compared with those obtained from comparative models. The results obtained by the contrast of the proposed analytical method and the Monte Carlo method. The impact of different model parameters and the coefficient of variation (CV) on reliability indices are discussed in the case studies.

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“…In the work of Wang, there are presented methods to estimate the parameters of condition‐monitored equipment whose failure rate follows the Cox's time‐dependent proportional hazards model. Errors of measurements, instrumentation inaccuracy, interpretation mistakes and imperfect observations cause misinterpretation in degradation state.…”

Section: State‐of‐the‐art Literature Surveymentioning

confidence: 99%

System Condition Estimation Based on Selected Tribodiagnostic Data

Vališ

Žák

Pokora

2015

Qual. Reliab. Engng. Int.

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The aim of the paper is to estimate a system‐soft failure occurrence and residual technical life. When estimating a residual technical life statistically, usually a big amount of tribodiagnostic data is used. Data include the information about particles contained in oil that testifies to oil and system conditions. We focus here on the particles that we consider to be interesting. They are ferrum (Fe) and lead (Pb) as contact degradation product. By modelling the occurrence of particles in oil, we expect to determine the expected moment for soft failure occurrence or adequate moment to perform preventive maintenance. The way of our modelling is based on the specific characteristics of diffusion processes, namely the Wiener process with positive drift and Ornstein–Uhlenbeck process. Following the modelling results, we could judge hazard rate and set‐up principles of ‘CBM ‐ Condition Based Maintenance’ (CBM). However, the possibilities are much wider, because we can also plan operation, mission and reduce life cost. Copyright © 2015 John Wiley & Sons, Ltd.

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Parameter Estimation Methods for Condition-Based Maintenance With Indirect Observations

Cited by 53 publications

References 17 publications

Failure prediction of diesel engine based on occurrence of selected wear particles in oil

Failure prediction of diesel engine based on occurrence of selected wear particles in oil

Applications of survival functions to continuous semi-Markov processes for measuring reliability of power transformers

System Condition Estimation Based on Selected Tribodiagnostic Data

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