Data analytics case studies in the maintenance, repair and overhaul (MRO) industry

Maurice Pelt; Konstatnos Stamoulis; Asteris Apostolidis

doi:10.1051/matecconf/201930404005

Data analytics case studies in the maintenance, repair and overhaul (MRO) industry

Maurice Pelt, Konstatnos Stamoulis, Asteris Apostolidis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Data analytics seems a promising approach to address the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences in cooperation with the aviation industry has initiated a two-year applied research project to explore the possibilities of data mining. More than 25 cases have been studied at eight different MRO enterprises. The CRISP-DM methodology is applied to have a structural guideline throughout the project. The data within MROs were explored and prepared. Individual case studies conducted with statistical and machine learning methods, were successfully to predict among others, the duration of planned maintenance tasks as well as the optimal maintenance intervals, the probability of the occurrence of findings during maintenance tasks.

Original language	English
Title of host publication	MATEC Web of Conferences
Editors	S. Pantelakis , C. Charitidis
Number of pages	8
Volume	304
DOIs	https://doi.org/10.1051/matecconf/201930404005
Publication status	Published - 2019
Event	9th EASN International Conference on “Innovation in Aviation & Space” - Athens, Greece Duration: 3 Sept 2019 → 6 Sept 2019

Conference

Conference	9th EASN International Conference on “Innovation in Aviation & Space”
Country/Territory	Greece
City	Athens
Period	3/09/19 → 6/09/19

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@inproceedings{ffd814bd2fe340ddbe7f97ef0aa992b4,

title = "Data analytics case studies in the maintenance, repair and overhaul (MRO) industry",

abstract = "Data analytics seems a promising approach to address the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences in cooperation with the aviation industry has initiated a two-year applied research project to explore the possibilities of data mining. More than 25 cases have been studied at eight different MRO enterprises. The CRISP-DM methodology is applied to have a structural guideline throughout the project. The data within MROs were explored and prepared. Individual case studies conducted with statistical and machine learning methods, were successfully to predict among others, the duration of planned maintenance tasks as well as the optimal maintenance intervals, the probability of the occurrence of findings during maintenance tasks.",

author = "Maurice Pelt and Konstatnos Stamoulis and Asteris Apostolidis",

year = "2019",

doi = "10.1051/matecconf/201930404005",

language = "English",

volume = "304",

editor = "{Pantelakis }, S. and {Charitidis }, C.",

booktitle = "MATEC Web of Conferences",

note = "9th EASN International Conference on “Innovation in Aviation & Space” ; Conference date: 03-09-2019 Through 06-09-2019",

}

TY - GEN

T1 - Data analytics case studies in the maintenance, repair and overhaul (MRO) industry

AU - Pelt, Maurice

AU - Stamoulis, Konstatnos

AU - Apostolidis, Asteris

PY - 2019

Y1 - 2019

N2 - Data analytics seems a promising approach to address the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences in cooperation with the aviation industry has initiated a two-year applied research project to explore the possibilities of data mining. More than 25 cases have been studied at eight different MRO enterprises. The CRISP-DM methodology is applied to have a structural guideline throughout the project. The data within MROs were explored and prepared. Individual case studies conducted with statistical and machine learning methods, were successfully to predict among others, the duration of planned maintenance tasks as well as the optimal maintenance intervals, the probability of the occurrence of findings during maintenance tasks.

AB - Data analytics seems a promising approach to address the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences in cooperation with the aviation industry has initiated a two-year applied research project to explore the possibilities of data mining. More than 25 cases have been studied at eight different MRO enterprises. The CRISP-DM methodology is applied to have a structural guideline throughout the project. The data within MROs were explored and prepared. Individual case studies conducted with statistical and machine learning methods, were successfully to predict among others, the duration of planned maintenance tasks as well as the optimal maintenance intervals, the probability of the occurrence of findings during maintenance tasks.

U2 - 10.1051/matecconf/201930404005

DO - 10.1051/matecconf/201930404005

M3 - Conference contribution

VL - 304

BT - MATEC Web of Conferences

A2 - Pantelakis , S.

A2 - Charitidis , C.

T2 - 9th EASN International Conference on “Innovation in Aviation & Space”

Y2 - 3 September 2019 through 6 September 2019

ER -

Data analytics case studies in the maintenance, repair and overhaul (MRO) industry

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