Determining the maximum periodic inspection interval for medium voltage motors using a Markov model

Picciolo, G.; Galli, Francesco; Biamonti, Alessio; Magni, Paolo

doi:10.1080/09537280802034224

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…In Fleming (2004), a Markov model is shown to be very useful in predicting the impact of alternative inspection strategies and leak detection on piping system reliability; additionally, it can evaluate modifications in risk due to changes in the in-service inspection programmes and in leak detection. Picciolo et al (2008) use such a model to identify the maximum periodic inspection interval in engines. Tabandeh et al (2016) use the Markov chain to model the uncertainty of demand response resources to power reduction.…”

Section: Markov Chainsmentioning

confidence: 99%

Maintenance strategy selection in electric power distribution systems

Carnero

Gómez

2017

Energy

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Section: Markov Chainsmentioning

confidence: 99%

Maintenance strategy selection in electric power distribution systems

Carnero

Gómez

2017

Energy

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“…Al-Najjar and Alsyouf (2003) propose a conceptual model using fuzzy logic and multiple criteria to select cost effective maintenance approaches; Wang et al (2007) suggest a model for the selection of maintenance strategies using the fuzzy variation of AHP. Oyarbide-Zubillaga et al (2008) use discrete event simulation to optimise preventive maintenance (PM); Picciolo et al (2008) use Markov modelling to optimise periodic inspection. To select a maintenance strategy more generally, Bashiri et al (2011) propose using a fuzzy linear assignment method, whilst Pourjavad and Shirouyehzad (2014) propose the fuzzy analytic network process (ANP).…”

Section: Introductionmentioning

confidence: 99%

Maintenance strategies: Decision Making Grid vs Jack-Knife Diagram

Seecharan

Labib²,

Jardine

2018

JQME

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Purpose Maintenance management is a vital strategic task given the increasing demand on sustained availability of machines. Machine performance depends primarily on frequency and downtime; therefore, ranking critical machines based on these two criteria is important to determine the appropriate maintenance strategy. The purpose of this paper is to compare two methods, using case studies, to allocate maintenance strategies while prioritising performance based on frequency and downtime or Mean Time to Repair: the Decision Making Grid (DMG) and Jack-Knife Diagram (JKD). Design/methodology/approach The literature indicates the need for an approach able to integrate maintenance performance and strategy in order to adapt existing data on equipment failures and to routinely adjust preventive measures. Maintenance strategies are incomparable; one strategy should not be applied to all machines, nor all strategies to the same machine. Findings Compared to the Pareto histogram, the DMG and JKD provide visual representations of the performance of the worst machines with respect to frequency and downtime, thus allowing maintenance technicians to apply the appropriate maintenance strategy. Each method has its own merits. Research limitations/implications This work compares only two methods based on their original conceptualisation. This is due to their similarities in using same input data and their main features. However, there is a scope to compare to other methods or variations of these methods. Practical implications This paper highlights how the DMG and JKD can be incorporated in industrial applications to allocate appropriate maintenance strategy and track machine performance over time. Originality/value Neither DMG nor JKD have been compared in the literature. Currently, the JKD has been used to rank machines, and the DMG has been used to determine maintenance strategies.

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“…This includes a vast majority of practical cases reported in the literature, such as Qiang et al (2013) for the girder of a crane, Sathyanarayanan et al (2008) for pavement marking retroreflectivity data, and Picciolo et al (2008) for an asynchronous medium-voltage squirrel motor. Also, our results can be utilized in the medical science and health care domain where individuals are scheduled to be seen at periodic assessment times (Chen et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Predictive analytics using a nonhomogeneous semi-Markov model and inspection data

et al. 2014

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Predicting the remaining useful life plays an important role in minimizing the overall maintenance cost of mechanical systems. Although most conventional reliability models deal with binary systems to perform such predictions, in most practical cases, mechanical systems experience multiple levels of degradation states before failure. When the degradation level associated with such a multistate deteriorating process is monitored only at fixed inspection points, extracted monitoring data are interval-censored. Interval censoring can influence both the parameter estimation (model training) and the calculation of principal reliability measures. This article studies the problem of parameter estimation and the development of principal prognostic-based reliability measures, including reliability function and mean residual life, for a multistate device under limited inspection capacity. The correctness of the introduced models is demonstrated through simulation-based numerical experiments. Finally, an example of the wear process of the shell of a bearing is used to demonstrate the application of the proposed models.

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Determining the maximum periodic inspection interval for medium voltage motors using a Markov model

Cited by 7 publications

References 12 publications

Maintenance strategy selection in electric power distribution systems

Maintenance strategy selection in electric power distribution systems

Maintenance strategies: Decision Making Grid vs Jack-Knife Diagram

Predictive analytics using a nonhomogeneous semi-Markov model and inspection data

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