Predictive maintenance modelling for through-life engineering services

C. Okoh, R. Roy, J. Mehnen

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
10 Downloads (Pure)


Predictive maintenance needs to forecast the numbers of rejections at any overhaul point before any failure occurs in order to accurately and proactively take adequate maintenance action. In healthcare, prediction has been applied to foretell when and how to administer medication to improve the health condition of the patient. The same is true for maintenance where the application of prognostics can help make better decisions. In this paper, an overview of prognostic maintenance strategies is presented. The proposed data-driven prognostics approach employs a statistical technique of (i) the parameter estimation methods of the time-to-failure data to predict the relevant statistical model parameters and (ii) prognostics modelling incorporating the reliability Weibull Cumulative Distribution Function to predict part rejection, replacement, and reuse. The analysis of the modelling uses synthetic data validated by industry domain experts. The outcome of the prediction can further proffer solution to designers, manufacturers and operators of industrial product-service systems. The novelty in this paper is the development of the through-life performance approach. The approach ascertains when the system needs to undergo maintenance, repair and overhaul before failure occurs.
Original languageEnglish
Pages (from-to)196–201
Number of pages6
JournalProcedia CIRP
Early online date2 Mar 2017
Publication statusPublished - 2017


  • through-life engineering services
  • maintenance strategies
  • data-driven prediction
  • predictive modelling
  • parameter estimation


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