Predictive Maintenance Approaches: A Systematic Literature Review

Since increasing attention has been given to predictive maintenance (PdM) of industrial equipment, in order to enhance operational efficiency, improve reliability, and reduce downtime, this powerful strategy offers significant benefits, holds clearly great promises, and is now regarded as a key for future perspective in Industry 4.0. There are various approaches to PdM, each offering its own set of advantages and disadvantages which are single and hybrid approaches to carrying out diagnostics and prognostics in PdM. In this paper we will compare these approaches according to different aspects such as complexity of data and interpretability of results. Moreover, we also discuss the barriers to successful adoption, such as data quality, system complexity, and the need for workforce training. Finally, this paper concludes by identifying future research directions in response to scientific problems, which will drive the next wave of innovation in predictive maintenance solutions.

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Publication Details

Published
2025-11-11
DOI
https://doi.org/10.3390/engproc2025112070
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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Predictive Maintenance Approaches: A Systematic Literature Review

Imane Satauri, Omar El Beqqali, Youssef Charkaoui, Dounia Tebr et al.
Machine Fault Diagnosis Techniques
article

Predictive Maintenance Approaches: A Systematic Literature Review

Imane Satauri, Omar El Beqqali, Youssef Charkaoui, Dounia Tebr, Zeineb El Hammoumi
article en

Abstract

Since increasing attention has been given to predictive maintenance (PdM) of industrial equipment, in order to enhance operational efficiency, improve reliability, and reduce downtime, this powerful strategy offers significant benefits, holds clearly great promises, and is now regarded as a key for future perspective in Industry 4.0. There are various approaches to PdM, each offering its own set of advantages and disadvantages which are single and hybrid approaches to carrying out diagnostics and prognostics in PdM. In this paper we will compare these approaches according to different aspects such as complexity of data and interpretability of results. Moreover, we also discuss the barriers to successful adoption, such as data quality, system complexity, and the need for workforce training. Finally, this paper concludes by identifying future research directions in response to scientific problems, which will drive the next wave of innovation in predictive maintenance solutions.

Sidi Mohamed Ben Abdellah University (MA)
Openalex Percentile: Top 58%
Machine Fault Diagnosis Techniques
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