Predictive maintenance is data-driven fault diagnosis and prognosis technique designed to reduce unscheduled maintenance and increase aircraft in-service operations time. The traditional approach of assessing predictive maintenance models in the aviation industry is based on a technical score like accuracy, precision, recall and F-metrics. Nevertheless, the model with the highest technical score does not necessarily lead to the highest financial savings and even can result in the additional costs for the business. At the same time, the highest financial saving in aircraft predictive maintenance could be achieved through the predictive model with a relatively small technical score.

    In this paper we analyse the efficiency of using the financial savings target function compared to technical scores, describes the boundaries of each approach’s application, and provides a systematic description of the methodology for calculating the financial savings function for maintenance, repair, and overhaul organizations.

    The approach proposed in the article supports maintenance, repair, and overhaul organizations increase efficiency in predictive maintenance activity, will increase repayment of predictive maintenance, and will provide a clear picture of how to allocate and prioritize resources in such projects.


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    Title :

    Objective Functions of Predictive Models in Maintenance, Repairs, and Overhaul Organizations


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:

    Conference:

    International Conference on Reliability and Statistics in Transportation and Communication ; 2020 ; Riga, Latvia October 14, 2020 - October 17, 2020



    Publication date :

    2021-02-07


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English