- Title
- Multi-classifier predictive maintenance strategy for a manufacturing plant
- Creator
- Singh, Prashant; Agrawal, Sunil; Chakraborty, Ayon
- Date
- 2021
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190858
- Identifier
- vital:17710
- Identifier
-
https://doi.org/10.1109/ICMIAM54662.2021.9715224
- Abstract
- Predictive Maintenance Management in an industry can play a pivotal role in asset management and revenue generation. This work proposes a data-driven-based multi classifier model for implementing predictive maintenance to simultaneously reduce the downtime and idle time of the machines in a manufacturing plant. A case study of the plant comprising of 100 machines has been done to identify the early prediction of failure, its nature, and the attributing cause. Gradient Boosting Tree Classifier and Random Forest Classifier machine learning algorithms have been used to develop the models for fault prediction. A comparative analysis of results obtained using these methods has also been done. Random Forest Classifier outperforms Gradient Boost tree classifier in all evaluation parameters - accuracy, precision and recall.
- Publisher
- IEEE
- Relation
- 2021 International Conference on Maintenance and Intelligent Asset Management (ICMIAM), Ballarat, Australia, 12-15 December 2021 p. 1-4
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright IEEE
- Subject
- Asset management; Boosting; Classifier; Data Driven Method; Diagnostics; Industries; Machine learning algorithms; Manufacturing; Predictive Maintenance; Predictive models; Prognostics; Random forests
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