- Title
- A study on the use of machine learning methods to improve reciprocating compressor reliability via torque tailoring
- Creator
- Lu, Kui; Sultan, Ibrahim; Phung, Truong
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189061
- Identifier
- vital:17379
- Identifier
-
https://doi.org/10.1109/ICMIAM54662.2021.9715205
- Identifier
- ISBN:9781665466714 (ISBN)
- Abstract
- Reciprocating compressors have found popularity in applications where compressed air is required at high pressure levels with moderate flow rates. The mechanical drives used for these compressors are based on the traditional slider-crank linkage which, despite its simplicity, does not lend itself to optimization effort aimed at improving the compressor reliability and performance. The work presented in this paper adopts the notion that the mechanical reliability of the compressor drive is limited by the level and cyclical variability of the loads transmitted through its members and the effectiveness of its cooling system. In the paper, machine learning methods will be employed to craft an objective function suitable to use in a Bayesian optimization effort undertaken to produce a more reliable compressor drive. A numerical example is presented to prove the validity of the presented method and its suitability for use in real life compressor designs. © 2021 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021, Ballarat, Australia, 12-15 December 2021, 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 IEEE
- Rights
- Open Access
- Subject
- Bayesian optimization; Compressor reliability; Principal Component Analysis; Reciprocating compressor
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