Trend analysis of the power law process using Expectation–Maximization algorithm for data censored by inspection intervals

Trend analysis is a common statistical method used to investigate the operation and changes of a repairable system over time. This method takes historical failure data of a system or a group of similar systems and determines whether the recurrent failures exhibit an increasing or decreasing trend. M...

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Vydané v:Reliability engineering & system safety Ročník 96; číslo 10; s. 1340 - 1348
Hlavní autori: Taghipour, Sharareh, Banjevic, Dragan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.10.2011
Elsevier
Predmet:
Algorithms
Applied sciences
Censoring
EM algorithm
Exact sciences and technology
Failure
Failure times
Intervals
Mathematics
Maximum likelihood
Medical infusion pump
Operational research and scientific management
Operational research. Management science
Parametric inference
Power law
Power law process
Probability and statistics
Queuing theory. Traffic theory
Reliability theory. Replacement problems
Sciences and techniques of general use
Statistical methods
Statistics
Trend analysis
Trend test
Trends
Trend test
Maximum likelihood
Medical infusion pump
Censoring
Power law process
EM algorithm
Recurrence
Data analysis
Statistical analysis
Censored sample
Censorship
Maintenance
Failures
Poisson process
Pump
Break up time
Likelihood ratio test
Trend analysis
Recursive method
Hospital
Repair
Power law
Repairable system
ISSN:0951-8320, 1879-0836
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Shrnutí:Trend analysis is a common statistical method used to investigate the operation and changes of a repairable system over time. This method takes historical failure data of a system or a group of similar systems and determines whether the recurrent failures exhibit an increasing or decreasing trend. Most trend analysis methods proposed in the literature assume that the failure times are known, so the failure data is statistically complete; however, in many situations, such as hidden failures, failure times are subject to censoring. In this paper we assume that the failure process of a group of similar independent repairable units follows a non-homogenous Poisson process with a power law intensity function. Moreover, the failure data are subject to left, interval and right censoring. The paper proposes using the likelihood ratio test to check for trends in the failure data. It uses the Expectation–Maximization (EM) algorithm to find the parameters, which maximize the data likelihood in the case of null and alternative hypotheses. A recursive procedure is used to solve the main technical problem of calculating the expected values in the Expectation step. The proposed method is applied to a hospital's maintenance data for trend analysis of the components of a general infusion pump.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2011.03.018