Research Article Open Access

Data Preparation in Machine Learning for Condition-based Maintenance

Ons Masmoudi1, Mehdi Jaoua1, Amel Jaoua2 and Soumaya Yacout2
  • 1 École Polytechnique de Montréal, Canada
  • 2 University of Tunis El Manar, Tunisia


Using Machine Learning (ML) prediction to achieve a successful, cost-effective, Condition-Based Maintenance (CBM) strategy has become very attractive in the context of Industry 4.0. In other fields, it is well known that in order to benefit from the prediction capability of ML algorithms, the data preparation phase must be well conducted. Thus, the objective of this paper is to investigate the effect of data preparation on the ML prediction accuracy of Gas Turbines (GTs) performance decay. First a data cleaning technique for robust Linear Regression imputation is proposed based on the Mixed Integer Linear Programming. Then, experiments are conducted to compare the effect of commonly used data cleaning, normalization and reduction techniques on the ML prediction accuracy. Results revealed that the best prediction accuracy of GTs decay, found with the k-Nearest Neighbors ML algorithm, considerately deteriorate when changing the data preparation steps and/or techniques. This study has shown that, for effective CBM application in industry, there is a need to develop a systematic methodology for design and selection of adequate data preparation steps and techniques with the proposed ML algorithms.

Journal of Computer Science
Volume 17 No. 6, 2021, 525-538


Submitted On: 15 December 2020 Published On: 4 June 2021

How to Cite: Masmoudi, O., Jaoua, M., Jaoua, A. & Yacout, S. (2021). Data Preparation in Machine Learning for Condition-based Maintenance. Journal of Computer Science, 17(6), 525-538.

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  • Data Preparation
  • Machine Learning
  • Condition-Based Maintenance
  • Performance Decay
  • Prediction