Maintenance and inspection as risk factors in helicopter accidents: Analysis and recommendations

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Abstract

In this work, we establish that maintenance and inspection are a risk factor in helicopter accidents. Between 2005 and 2015, flawed maintenance and inspection were causal factors in 14% to 21% of helicopter accidents in the U.S. civil fleet. For these maintenance-related accidents, we examined the incubation time from when the maintenance error was committed to the time when it resulted in an accident. We found a significant clustering of maintenance accidents within a short number of flight-hours after maintenance was performed. Of these accidents, 31% of these accidents occurred within the first 10 flight-hours. This is reminiscent of infant mortality in reliability engineering, and we characterized it as maintenance error infant mortality. The last quartile of maintenance-related accidents occurred after 60 flight-hours following maintenance and inspection. We then examined the “physics of failures” underlying maintenance-related accidents and analyzed the prevalence of different types of maintenance errors in helicopter accidents. We found, for instance, that the improper or incomplete (re)assembly or installation of a part category accounted for the majority of maintenance errors with 57% of such cases, and within this category, the incorrect torquing of the B-nut and incomplete assembly of critical linkages were the most prevalent maintenance errors. We also found that within the failure to perform a required preventive maintenance and inspection task category, the majority of the maintenance programs were not executed in compliance with federal regulations, nor with the manufacturer maintenance plan. Maintenance-related accidents are particularly hurtful for the rotorcraft community, and they can be eliminated. This is a reachable objective when technical competence meets organizational proficiency and the collective will of all the stakeholders in this community. We conclude with a set of recommendations based on our findings, which borrow from the ideas underlying the defense-in-depth safety principle to address this disquieting problem.

Publication
PLOS ONE, 14(2)
Archana Tikayat Ray
Archana Tikayat Ray
atr@gatech.edu

My research interests include applied data science, machine learning, and Natural Language Processing.

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