Economic Efficiency of Data-Driven Fault Diagnosis and Prognosis Techniques in Maintenance and Repair Organizations

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 117)


Maintenance costs represent a significant part of economic expenses for the aviation industry. The development of data-driven fault diagnosis, prognosis, and health management techniques create unique opportunities to increase the efficiency of Maintenance and Repair Organization (MRO) activity and, as a result, to reduce airline maintenance costs. Nevertheless, despite the widespread data-driven prognosis ideas, the economic success of MRO in data-driven prognosis remains rather frugal and local. It is yet to demonstrate its positive strategic and long-term impact on corporate economics comparable to the one of the LEAN philosophy. Many MRO still do not know how to approach predictive maintenance techniques properly – due to lack of a consistent theory and strategy for the development of data-driven projects, and unavailability of a standard with proven mechanisms and methodology that would allow a MRO to make decisions on development of predictive projects and evaluate their economic performance. This paper reviews the existing literature related to the economic efficiency of data-driven projects, defines current gaps in economic analysis and proposes the methodology of how to support an MRO in dealing with predictive maintenance projects effectively on macro, semi-macro and micro levels.


Predictive maintenance Airplane health management Economic analysis Gaps Macroanalysis Microanalysis 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.S7 Engineering LLCMoscowRussia
  2. 2.Transport and Telecommunication InstituteRigaLatvia

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