A Review on Machine Learning Techniques for Predictive Maintenance in Industry 4.0

DOI

10.2991/978-94-6463-136-4_67How to use a DOI?

Keywords

Machine learning; predictive maintenance; manufacturing equipment; Industry 4.0; failure prediction

Abstract

Predictive maintenance is the process of continuously monitoring a system to prevent it from breaking down. Along with the traditional equipment maintenance which uses a periodic schedule instead of reacting to equipment failures, predictive maintenance predicts failure of an equipment. Adopting a suitable and reliable predictive maintenance strategy for equipment like automobile part manufacturing machines has remained a difficulty for industry. To minimize the unplanned downtime of a machine caused by its failure in highly automated production line is very challenging piece of predictive maintenance. Recently the Industry 4.0 concept is becoming more widely adopted in manufacturing around the world. The survey emphases on different methods available for predictive maintenance and the various data used in the researches. Machine learning promises the better solutions over the traditional maintenance problem. In this research, intelligent approach is presented which is to be used to design proposed PdM planning model. To predict the failure state with respect to down time a weight optimized GRU model is proposed. And Whale Optimization with Seagull Algorithm has to be used to optimize the weight in GRU based learning. Thus the results are well-suited for PdM planning and capable of accurately predicting future components for Mechanical part making machine.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Megha Sisode
AU  - Manoj Devare
PY  - 2023
DA  - 2023/05/01
TI  - A Review on Machine Learning Techniques for Predictive Maintenance in Industry 4.0
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 774
EP  - 783
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_67
DO  - 10.2991/978-94-6463-136-4_67
ID  - Sisode2023
ER  -