Early life failures and services of industrial asset fleets

Arinan Dourado and Felipe A.C. Viana

Reliability Engineering and System Safety, 2021, vol. 205, issue C

Abstract: In the service market targeting fleets of industrial assets (e.g., aircraft, jet engines, wind turbines, etc.), original equipment manufacturers and service providers compete with one another through offers covering day-to-day service as well as major maintenance and repairs over. Since decision-making is highly guided by reliability models, it is safe to say that services profitability dependents on the ability to understand the complex stochastic interactions between operating conditions and component capability. Unfortunately, factors such as aggressive mission mixes introduced by operators, exposure to a harsh environment, inadequate maintenance, and problems with mass production can lead to large discrepancies between predicted and observed useful lives. This paper focuses on the quantification of the infant mortality impact on fleets of industrial assets. A numerical experiment is used to study how the number of failure observations and fleet size impacts the modeling of fleet reliability. Dynamic Bayesian networks implementing physics-based models are used to model fleet unreliability considering the effects of bad batch of materials.The results demonstrate that material capability, penetration of bad batch of material in the fleet, and fleet size drastically influence the model accuracy. Therefore, small fleet operators, which naturally observe a lownumber of failures, have to deal with larger uncertainties when quantifying infant mortality. This negatively impacts their ability to allocate resources such as inventory, labor, and account for the loss of productivity while servicing their fleet. With large fleet operators, on the other hand, large number of failure observations can cause high financial burden. Nevertheless, it also allows for reduced uncertainty in building/updating the reliability models, which can help their ability to forecast future failures and make provisions for service and maintenance. Finally, the results also show that measures such as recommissioning of the fleet and inspection campaigns can mitigate the effects of fleet-wide early life problems.

Keywords: Fleet management; Fleet unreliability; Bayesian networks; Reliability; Prognosis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307262

DOI: 10.1016/j.ress.2020.107225

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