Abstract
Condition monitoring is one of the most important activities to implement predictive maintenance in industrial processes and perform fault diagnosis. Vibration is the most used signal for this purpose, however current signals arise as a non-intrusive alternative to condition monitoring. On the other hand, data driven approaches becomes as a way to develop fault classifiers by using Machine Learning. This paper proposes the development of a fault classifier for diagnosing failures in the bearings of a reciprocating compressor by using the current signals measured from the induction machine that power the mechanical device. The proposal applies cluster validity assessment for feature selection, and a LAMDA-based model for classification. Results show that this proposal can diagnose three failure modes with a precision over 90%.
Original language | English |
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Pages (from-to) | 199-204 |
Number of pages | 6 |
Journal | IFAC-PapersOnLine |
Volume | 55 |
Issue number | 19 |
DOIs | |
State | Published - 1 Jul 2022 |
Event | 5th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies, AMEST 2022 - Bogota, Colombia Duration: 26 Jul 2022 → 29 Jul 2022 |
Bibliographical note
Funding Information:Thanks to GIDTEC-UPS for supporting this work through the funds from related research projects.
Publisher Copyright:
Copyright © 2022 The Authors.
Keywords
- ANOVA
- cluster validity index
- Fault diagnosis
- feature selection
- fuzzy similarity
- reciprocating compressors