A systematic literature review of machine learning methods applied to predictive maintenance
By T. Carvalho, Fabrízzio Soares, R. Vita, R. Francisco, J. Basto, S. G. S. Alcalá. Computers & industrial engineering, 2019
1,257 citations recorded by the source corpus.
Abstract The amount of data extracted from production processes has increased exponentially due to the proliferation of sensing technologies. When processed and analyzed, data can bring out valuable information and knowledge from manufacturing process, production system and equipment. In industries, equipment maintenance is an important key, and affects the operation time of equipment and its efficiency. Thus, equipment faults need to be identified and solved, avoiding shutdown in the production processes. Machine Learning (ML) methods have been emerged as a promising tool in Predictive Maintenance (PdM) applications to prevent failures in equipment that make up the production lines in the factory floor. However, the performance of PdM applications depends on the appropriate choice of the ML method. The aim of this paper is to present a systematic literature review of ML methods applied to PdM, showing which are being explored in this field and the performance of the current state-of-the-art ML techniques. This review focuses on two scientific databases and provides a useful foundation on the ML techniques, their main results, challenges and opportunities, as well as it supports new research works in the PdM field.
Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/df71f242093cae84437e8fdc961c601e19d9afe2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Georgios Sermpinis
- Christoph Bergmeir
- Nikolaos Kourentzes
- Pierre Pinson
- Konstantinos Nikolopoulos
- David F. Hendry
- Devon K. Barrow
- Ricardo J. Bessa
In Safety, Risk, Reliability and Quality
Citations in the corpus
- Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 (2020, methodology): “Further details on the main steps involved can be found in [1].”
- Forecasting: theory and practice (2020, background): “Once any researcher or practitioner becomes acquainted to a certain degree with the SS technology, some important advanced issues in time series forecasting may be comfortably addressed (Casals et al., 2016).”
- Predictive Maintenance and Intelligent Sensors in Smart Factory: Review (2021, methodology): “In their overview, Carvalho [18] focuses on machine learning methods, which they consider a promising tool for predictive maintenance.”