Data Preparation in Machine Learning for Condition-based Maintenance

Ons Masmoudi,Mehdi Jaoua,Amel Jaoua,Soumaya Yacout

doi:10.3844/jcssp.2021.525.538

Abstract

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.

Highlights

Under the Industry 4.0 paradigm, reliability of industrial assets and production machines is very important
This paper investigates the effect of different data preparation steps and techniques on the Machine Learning (ML) prediction accuracy of Gas Turbines (GTs) performance decay
An accurate degradation prediction model of GTs performance is highly desired to reach an effective ConditionBased Maintenance (CBM) strategy which predicts the degradation of the propulsion plant over time and schedule maintenance in advance

Summary

Introduction

Under the Industry 4.0 paradigm, reliability of industrial assets and production machines is very important. Machine health monitoring and management aims to operate with near zero breakdown. In this context, (Aivaliotis et al, 2019) have proposed a methodology based on the Digital Twin concept in order to enable predictive maintenance for manufacturing systems using Prognostics and Health Management techniques. Based on collected sensors data, ML intelligent predictive algorithms are implemented to reach successful ConditionBased Maintenance (CBM) strategy. This success is mainly related to the capability of such ML algorithms to handle high dimensional and multivariate data from various sensors and predict the degradation and future failure states (Accorsi et al, 2017). In (Coraddu et al, 2016) authors have shown the potential of ML algorithms in predicting propulsive performance degradation of a naval vessel powered by Gas Turbines (GTs)

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Methods

Conclusion