Early Failure In Probabilistic Asset Fleet Management

Abstract

Despite continuous advances in design and manufacturing of industrial equipment, managing large fleets of engineering assets (e.g., thousands of jet engines or aircrafts) is challenging due to the large variation in operating conditions (duty cycle, environment, etc.) and component capability (e.g., material, manufacturing, and assembly variation). The importance of fleet reliability management is reflected in the very profitable market focused on services and warranties. Typically, large original equipment manufacturers and independent service providers compete with each other by offering contracts designed to cover events in day-to-day maintenance as well as major repairs over the life of the asset. This paper is focused on a probabilistic treatment of infant mortality in fleets of industrial assets. Factors such as aggressive mission mixes introduced by operators, exposure to harsh environment, inadequate maintenance, and problems with mass production (bad batch of materials) can lead to very reduced useful life. With the aid of a simple numerical experiment, one fundamental question is addressed: how does number of observations and fleet size interact with each other in fleet management? The results demonstrate that material capability and commissioning time can drastically influence fleet unreliability. Although these factors drive the actual aging of the commissioned fleet, the first failure observations depend on the actual fleet size (manifestation of early life as an outlier problem). The practical implication is that small fleet operators have to deal with large uncertainties when modeling/quantifying infant mortality. This impacts their ability to make provisions for service and maintenance (inventory, labor, loss of productivity, etc.). This problem is much less acute in large fleet operators (due to reduction in forecasted number of failures).

Publication Date

1-1-2018

Publication Title

2018 Multidisciplinary Analysis and Optimization Conference

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.2514/6.2018-3100

Socpus ID

85051648206 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85051648206

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