The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance
Abstract
The area of predictive maintenance has taken a lot of prominence in the last couple of years due to various reasons. With new algorithms and methodologies growing across different learning methods, it has remained a challenge for industries to adopt which method is fit, robust and provide most accurate detection. Fault detection is one of the critical components of predictive maintenance; it is very much needed for industries to detect faults early and accurately. In a production environment, to minimize the cost of maintenance, sometimes it is required to build a model with minimal or no historical data. In such cases, unsupervised learning would be a better option model building. In this paper, we have chosen a simple vibration data collected from an exhaust fan, and have fit different unsupervised learning algorithms such as PCA T2statistic, Hierarchical clustering, K-Means, Fuzzy C-Means clustering and model-based clustering to test its accuracy, performance, and robustness. In the end, we have proposed a methodology to benchmark different algorithms and choosing the final model.
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Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 914
Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0
Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020) · Sustainability · doi:10.3390/su12198211 · Cited by 834
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Condition Monitoring using Machine Learning: A Review of Theory, Applications, and Recent Advances
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Metadata
- Authors
- Amruthnath, N., & Gupta, T.
- Venue
- 2018 5th International Conference on Industrial Engineering and Applications (ICIEA)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 355-361
- OpenAlex
- W2808548605
- Cited by
- 265
- References
- 15
- FWCI
- 30.30
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Amruthnath, N., & Gupta, T. (2018). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. 2018 5th International Conference on Industrial Engineering and Applications (ICIEA). https://doi.org/10.1109/iea.2018.8387124
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
