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Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach
- Source :
- IEEE Transactions on Energy Conversion, IEEE Transactions on Energy Conversion, Institute of Electrical and Electronics Engineers, 2021, pp.1-9. ⟨10.1109/TEC.2021.3085909⟩
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- International audience; The monitoring of rolling element bearing is indexed as a critical task for condition-based maintenance in various industrial applications. It allows avoiding unscheduled maintenance operations while decreasing their cost. For this purpose, various methodologies were developed to ensure accurate and efficient monitoring. In this context, this paper proposes an approach for bearing faults early diagnosis based on the variational mode decomposition (VMD), used as a notch filter for dominant mode cancellation, and a machine learning approach, namely the one-dimensional convolution neural network (1D-CNN), for detection and diagnosis purposes. Specifically, the proposed approach first performs features extraction using VMD for fault detection, and then triggers to multi-scale features extraction using CNN convolution and pooling layers for classification and diagnosis. The proposed bearing faults detection and diagnosis approach is evaluated, in terms of robustness and performances, using the well-known Case Western Reserve University experimental dataset. In addition, performances are evaluated versus well- established demodulation techniques, in terms of faults detection, and machine learning strategies, in terms of faults diagnosis. The achieved results show that the proposed VMD notch filter-based 1D-CNN approach is clearly promising for bearing degradations monitoring.
- Subjects :
- Computer science
variational mode decomposition
Energy Engineering and Power Technology
Context (language use)
02 engineering and technology
Fault (power engineering)
Machine learning
computer.software_genre
Convolutional neural network
Fault detection and isolation
Convolution
law.invention
convolution neural network
Robustness (computer science)
law
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Bearing (mechanical)
business.industry
[SPI.NRJ]Engineering Sciences [physics]/Electric power
fault detection and diagnosis
021001 nanoscience & nanotechnology
machine learning
bearing fault
Rolling-element bearing
020201 artificial intelligence & image processing
Artificial intelligence
0210 nano-technology
business
computer
Subjects
Details
- ISSN :
- 15580059 and 08858969
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Energy Conversion
- Accession number :
- edsair.doi.dedup.....afaf40bde3c3b7ed37f96e322a08ecfb