Estimation of thin-oxide reliability using proportional hazards models

Elsayed, Elsayed A.; Chan, C.K.

doi:10.1109/24.103013

Cited by 48 publications

(16 citation statements)

References 20 publications

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“…Compared with this model, the Cox proportional hazard model is a semi-parametric model in that it does not need to assume and thus defend any distribution for the hazard rate, which will benefit us because assuming hazard rate functions for the field data could hide some useful information although it could provide some conveniences (George, 2003). Another advantage of Cox's model is that studying interactions between variables is easy (Elsayed and Chan, 1990;Hougaard, 1999). Now if we have a simple event sequence as shown in Fig.…”

Section: Failure Event Prediction Using the Cox Proportional Hazard Mmentioning

confidence: 98%

Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures

Zhou

Choubey³

et al. 2007

IIE Transactions

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The analysis of event sequence data that contains system failures is becoming increasingly important in the design of service and maintenance policies. This paper presents a systematic methodology to construct a statistical prediction model for failure event based on event sequence data. First, frequent failure signatures, defined as a group of events/errors that repeatedly occur together, are identified automatically from the event sequence by use of an efficient algorithm. Then, the Cox proportional hazard model, that is extensively used in biomedical survival analysis, is used to provide a statistically rigorous prediction of system failures based on the time-to-failure data extracted from the event sequences. The identified failure signatures are used to select significant covariates for the Cox model, i.e., only the events and/or event combinations in the signatures are treated as explanatory variables in the Cox model fitting. By combining the failure signature and Cox model approaches the proposed method can effectively handle the situation of a long event sequence and a large number of event types in the sequence. Its effectiveness is illustrated by a numerical study and analysis of real-world data. The proposed method can help proactively diagnose machine faults with a sufficient lead time before actual system failures to allow preventive maintenance to be scheduled thereby reducing the downtime costs.

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Section: Failure Event Prediction Using the Cox Proportional Hazard Mmentioning

confidence: 98%

Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures

Zhou

Choubey³

et al. 2007

IIE Transactions

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“…They also applied PHM to study the reliability of repairable systems considering the effect of operating conditions with an example of thermal power unit. Elsayed and Chan (1990) developed a PHM method to estimate thin oxide dielectric reliability and time-dependent dielectric breakdown hazard rates, including two groups of models: group one doesn't consider interactions between temperature and electric field, while group two analyzes interactions between these two factors. JoWiak (1992) developed an approach to utilize PHM in reliability exploration of microcomputer systems.…”

Section: Applications In Reliability Analysismentioning

confidence: 99%

Condition based maintenance optimization considering multiple objectives

Tian

Lin

2009

J Intell Manuf

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In condition based maintenance (CBM) optimization, the main optimization objectives include maximizing reliability and minimizing maintenance costs, which are often times conflicting to each other. In this work, we develop a physical programming based approach to deal with the multi-objective condition based maintenance optimization problem. Physical programming presents two major advantages: (1) it is an efficient approach to capture the decision makers' preferences on the objectives by eliminating the iterative process of adjusting the weights of the objectives, and (2) it is easy to use in that decision makers just need to specify physically meaningful boundaries for the objectives. The maintenance cost and reliability objectives are calculated based on proportional hazards model and a control limit CBM replacement policy. With the proposed approach, the decision maker can systematically and efficiently make good tradeoff between the cost objective and reliability objective. An example is used to illustrate the proposed approach.

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“…Once the values of k(z j ) are obtained, we draw the plot of k(z j ) versus z j . The function form of k(z) can be specified on the basis of the physical model 13,14 for some typical stress such as temperature and voltage or by examining the shape of the plot. In the latter case, one can consider more than one candidate model and the best one can be determined on the basis of the goodness-of-fit or the Akaike's information criterion (AIC) or its extensions 15 if the numbers of the parameters of candidate models are different.…”

Section: Modelling G(z)mentioning

confidence: 99%

A General Proportional Model and Modelling Procedure

Jiang

2012

Quality & Reliability Eng

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This paper presents a unified representation for the Cox model and its extensions (e.g. proportional intensity model, proportional wear‐out model and accelerated life testing model) and calls it the general proportional model. A multi‐step procedure is proposed to sequentially determine the three parts of the model (i.e. baseline, covariate and stochastic parts). The basic idea of the proposed approach is to use an optimization method to estimate the ‘accelerated factor’ associated with each ‘stress’ (i.e. covariate) level. The estimated accelerated factors are fitted to an adequate model as a function of the stress level. The fitted accelerated factor model is used to transform the observed sample into an equivalent (or mixed) sample, which corresponds to a specified reference stress level. The main advantages of the proposed approach are that it makes the modelling process flexible and transparent, avoids making possibly unrealistic assumptions on the forms (or distribution family) of relevant functions (or random variable) and fully utilizes all the observations for modelling. The appropriateness and usefulness of the model and modelling approach are illustrated through a detailed analysis for two real‐world examples published in the literature. Copyright © 2012 John Wiley & Sons, Ltd.

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Estimation of thin-oxide reliability using proportional hazards models

Cited by 48 publications

References 20 publications

Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures

Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures

Condition based maintenance optimization considering multiple objectives

A General Proportional Model and Modelling Procedure

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