Predictive Maintenance in Aviation: Failure Prediction from Post-Flight Reports. Korvesis, P., Besseau, S., & Vazirgiannis, M. In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pages 1414–1422, April, 2018. ISSN: 2375-026Xdoi abstract bibtex In this paper we present an approach to tackle the problem of event prediction for the purpose of performing predictive maintenance in aviation. Given a collection of recorded events that correspond to equipment failures, our method predicts the next occurrence of one or more events of interest (target events or critical failures). Our objective is to develop an alerting system that would notify aviation engineers well in advance for upcoming aircraft failures, providing enough time to prepare the corresponding maintenance actions. We formulate a regression problem in order to approximate the risk of occurrence of a target event, given the past occurrences of other events. In order to achieve the best results we employed a multiple instance learning scheme (multiple instance regression) along with extensive data preprocessing. We applied our method on data coming from a fleet of aircraft and our predictions involve failures of components onboard, specifically components that are related to the landing gear. The event logs correspond to post flight reports retrieved from multiple aircraft during several years of operation. To the best of our knowledge, this paper is the first attempt on aircraft failure prediction using post flight report data and finally, our findings show high potential impact on the aviation industry.
@inproceedings{korvesis_predictive_2018,
title = {Predictive {Maintenance} in {Aviation}: {Failure} {Prediction} from {Post}-{Flight} {Reports}},
shorttitle = {Predictive {Maintenance} in {Aviation}},
doi = {10.1109/ICDE.2018.00160},
abstract = {In this paper we present an approach to tackle the problem of event prediction for the purpose of performing predictive maintenance in aviation. Given a collection of recorded events that correspond to equipment failures, our method predicts the next occurrence of one or more events of interest (target events or critical failures). Our objective is to develop an alerting system that would notify aviation engineers well in advance for upcoming aircraft failures, providing enough time to prepare the corresponding maintenance actions. We formulate a regression problem in order to approximate the risk of occurrence of a target event, given the past occurrences of other events. In order to achieve the best results we employed a multiple instance learning scheme (multiple instance regression) along with extensive data preprocessing. We applied our method on data coming from a fleet of aircraft and our predictions involve failures of components onboard, specifically components that are related to the landing gear. The event logs correspond to post flight reports retrieved from multiple aircraft during several years of operation. To the best of our knowledge, this paper is the first attempt on aircraft failure prediction using post flight report data and finally, our findings show high potential impact on the aviation industry.},
booktitle = {2018 {IEEE} 34th {International} {Conference} on {Data} {Engineering} ({ICDE})},
author = {Korvesis, Panagiotis and Besseau, Stephane and Vazirgiannis, Michalis},
month = apr,
year = {2018},
note = {ISSN: 2375-026X},
keywords = {Aircraft, Electronic mail, Feature extraction, Hardware, Monitoring, Predictive maintenance, aviation, failure prediction, predictive maintenance},
pages = {1414--1422},
}
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