Proportional hazards modelling in the analysis of computer systems reliability

Walker, E.V.; Wightman, D.W.; Bendell, A.

doi:10.1016/0951-8320(88)90121-4

Cited by 16 publications

(6 citation statements)

References 12 publications

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“…The work of Walls and Bendell (1985) in exploratory data analysis, Bayesian methods (Bunday, 1991) and counting processes (Fleming and Harrington, 1991;Thompson, 1988;Crowder et al, 1991;Ansell and Phillips, 1989), which includes proportional intensities (Lawless, 1987), additive hazards (Pijnenberg, 1991), proportional hazards (Cox, 1972) and generalized linear models (McCullagh and Nelder, 1989), provide approaches which have aided the engineer or manager in understanding the possible causes of failure in systems. Examples of these analyses have been carried out by Drury et al (1987), Kumar and Klefsjo (1992), Lawless (1987) and Bendell (1985, 1995).…”

Section: Reliabilitymentioning

confidence: 98%

The Future Role of Statistics in Quality Engineering and Management (with Discussion)

Bendell

Disney

McCollin

1999

J Royal Statistical Soc D

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Statisticians are concerned that the business world does not regard statistical literacy, or statisticians, as important. Little progress has been made in spreading the belief in the power of statistics to the non-statistical. This problem is as strong in the context of supporting organizational and product quality improvement as it is elsewhere. We summarize the effect of Genichi Taguchi's work on industrial experimentation, as well as developments in the use of statistical process control, statistical reliability requirements and other statistical approaches to quality. The effect of developments in quality systems standards, such as BS EN ISO 9000, the European Business Excellence Award Model and the Baldrige Award, are also discussed. We argue that statisticians' attitudes to non-statisticians and the use of their discipline will have to change if statistics is to realize its potential in supporting quality improvement. Re-education is necessary not just for the non-statistician but for the statistical community as well.

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Section: Reliabilitymentioning

confidence: 98%

The Future Role of Statistics in Quality Engineering and Management (with Discussion)

Bendell

Disney

McCollin

1999

J Royal Statistical Soc D

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“…Over the past 15 years, these basic techniques have been refined, extrapolated and expanded, particularly for the purposes of optimizing maintenance decisions [113][114][115][116][117][118][119][120][121][122], analysing data from accelerated life tests [107], as well as to model systems subject to partial repair [123][124][125]. Specific examples of industry led research include [126][127][128][129]. A variation of PHM that does not assume perfect repair, known as the Proportional Intensity Model, has also been applied by several researchers, as discussed by Lugtigheid et al [10], although to date it has been predominantly been applied for optimizing maintenance decisions.…”

Section: Proportional Hazards Modellingmentioning

confidence: 99%

Prognostic modelling options for remaining useful life estimation by industry

Sikorska¹,

Hodkiewicz

2011

Mechanical Systems and Signal Processing

782

513

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“…Over the past years, these basic techniques have been refined, extrapolated and expanded, particularly for the purposes that include optimizing maintenance decisions [90,91,92,93,94,95,96,97,82,98], analysing data obtained from accelerated life tests [83], and appliction to model systems that are subject to partial repair [99,100,101]. Specific examples of industry led research include [102,103,104,105].…”

Section: Proportional Hazards Modellingmentioning

confidence: 99%

Survey of prognostics methods for condition-based maintenance in engineering systems

Taheri,

Kolmanovsky,

Gusikhin

2019

Preprint

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It is not surprising that the idea of efficient maintenance algorithms (originally motivated by strict emission regulations, and now driven by safety issues, logistics and customer satisfaction) has culminated in the socalled condition-based maintenance program. Condition-based program/monitoring consists of two major tasks, i.e., diagnostics and prognostics each of which has provided the impetus and technical challenges to the scientists and engineers in various fields of engineering. Prognostics deals with the prediction of the remaining useful life, future condition, or probability of reliable operation of an equipment based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce the downtime, spares inventory, maintenance costs, and safety hazards. Given the significance of prognostics capabilities and the maturity of condition monitoring technology, there have been an increasing number of publications on machinery prognostics in the past few years. These publications cover a wide range of issues important to prognostics. Fortunately, improvement in computational resources technology has come to the aid of engineers by presenting more powerful onboard computational resources to make some aspects of these new problems tractable. In addition, it is possible to even leverage connected vehicle information through cloud-computing. Our goal is to provide a report on the state of the art and to summarize some of the recent advances in prognostics with the emphasis on models, algorithms and technologies used for data processing and decision making.

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Proportional hazards modelling in the analysis of computer systems reliability

Cited by 16 publications

References 12 publications

The Future Role of Statistics in Quality Engineering and Management (with Discussion)

The Future Role of Statistics in Quality Engineering and Management (with Discussion)

Prognostic modelling options for remaining useful life estimation by industry

Survey of prognostics methods for condition-based maintenance in engineering systems

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