Data visualization for reliability analysis of repairable systems

Gámiz, María Luz; López-Montoya, Antonio J.; Martínez-Miranda, María Dolores; Miranda, Rocío Raya

doi:10.1002/qre.2383

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…The occurrence of the system failure can be expressed as a process stochastic variable noted T1,T2,.......,Tn where Ti represents the arrival time of the failure. The set of time intervals X1,X2,.......,Xn where Xi=Ti−Ti−1 are generally neither independent nor identical whose function of the conditional rate or the intensity of failure can be given as follows (Gámiz et al , 2018, pp. 99–115):where H ( t ) represents the history of the process up to time t , with t not included, and N ( t ) is the number of time intervals t .…”

Section: Models’ Specificationmentioning

confidence: 99%

Assessment of the effectiveness of corrective maintenance of an oil pump using the proportional intensity model (PIM)

Bacha

Boutarfaïa

Dron³

2021

IJQRM

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PurposeComplex repairable systems (CRSs) are generally modeled by stochastic processes called “point processes.” These are generally summed up in the nonhomogeneous Poisson process (NHPP) and the renewal process (RP), which represent the minimum and maximum repair, respectively. However, the industrial environment affects systems in some way. This is why the main objective of this work is to model the CRS with a concept reflecting the real state of the system by incorporating an indicator in the form of covariate. This type of model, known as the proportional intensity model (PIM), will be analyzed with simulated failure data to understand the behavior of the failure process, and then it will be tested for real data from a petroleum company to evaluate the effectiveness of corrective actions carried out.Design/methodology/approachTo solve the partial repair modeling problem, the PIM was used by introducing, on the basis of the NHPP model, a multiplicative scaling factor, which reflects the degree of efficiency after each maintenance action. Several values of this multiplicative factor will be considered to generate data. Then, based on the reliability and maintenance history of 12-year pump's operation obtained from the SONATRACH Company (south industrial center (CIS), Hassi Messaoud, Algeria), the performance of the PIM will be judged and compared with the model of NHPP and RP in order to demonstrate its flexibility in modeling CRS. Using the maximum likelihood approach and relying on the Matlab software, the best fitting model should have the largest likelihood value.FindingsThe use of the PIM allows a better understanding of the physical situation of the system by allowing easy modeling to apply in practice. This is expressed by the value which, in this case, represents an improvement in the behavior of the system provided by a good quality of the corrective maintenance performed. This result is based on the hypothesis that modeling with the PIM can provide more clarification on the behavior of the system. It can indicate the effectiveness of the maintenance crew and guide managers to confirm or revise their maintenance policy.Originality/valueThe work intends to reflect the real situation in which the system operates. The originality of the work is to allow the consideration of covariates influencing the behavior of the system during its lifetime. The authors focused on modeling the degree of repair after each corrective maintenance performed on an oil pump. Since PIM does not require a specific reliability distribution to apply it, it allows a wide range of applications in the various industrial environments. Given the importance of this study, the PIM can be generalized for more covariates and working conditions.

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Section: Models’ Specificationmentioning

confidence: 99%

Assessment of the effectiveness of corrective maintenance of an oil pump using the proportional intensity model (PIM)

Bacha

Boutarfaïa

Dron³

2021

IJQRM

View full text Add to dashboard Cite

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Data Visualization in Reliability of Repairable Systems: The SiZer Map Tool

Gámiz

López-Montoya

Martínez-Miranda

et al. 2020

Wiley StatsRef: Statistics Reference Online

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In this article, the graphical exploratory tool SiZer Map is constructed for the inference of the rate of occurrences of events that follow a nonhomogeneous Poisson process. SiZer Map is defined considering nonparametric local linear kernel estimators for the rate function and its first derivative. The bandwidth parameter is the viewing scale, and the considered time interval is the localization space. The shape characteristics of the rate function are distinguished from those which are merely an artifact of the sampling variability of the data through confidence intervals for the first derivative. The proposal is illustrated with some real data sets that represent recurrent events in time.

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Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

Yousefi

Coit

Zhu

2020

Computers & Industrial Engineering

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In this paper, a repairable multi-component system is studied where all the components can be repaired individually within the system. The whole system is inspected at inspection intervals and the failed components are detected and replaced with a new one, while the other components continue functioning. Replacing components individually within the system makes their initial age to be different at each inspection time. Different initial age of all the components have affect on the system reliability and probability of failure and consequently the optimal inspection time, which is for the whole system not individual components. A dynamic maintenance policy is proposed to find the next inspection time based on the initial age of all the components. Two competing failure processes of degradation and a shock process are considered for each component. In our paper, there is a mutual dependency between the degradation process and shock process. Each incoming shock adds additional abrupt damages on the cumulative degradation path of all the components, and the shock arrival process is affected by the system degradation process.A realistic numerical example is presented to illustrate the proposed reliability and maintenance model.

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Data visualization for reliability analysis of repairable systems

Cited by 5 publications

References 37 publications

Assessment of the effectiveness of corrective maintenance of an oil pump using the proportional intensity model (PIM)

Assessment of the effectiveness of corrective maintenance of an oil pump using the proportional intensity model (PIM)

Data Visualization in Reliability of Repairable Systems: The SiZer Map Tool

Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

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