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Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks
- Source :
- Chinese Journal of Mechanical Engineering, Vol 37, Iss 1, Pp 1-19 (2024)
- Publication Year :
- 2024
- Publisher :
- SpringerOpen, 2024.
-
Abstract
- Abstract The co-frequency vibration fault is one of the common faults in the operation of rotating equipment, and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment. In engineering scenarios, co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify, and existing intelligent methods require more hardware conditions and are exclusively time-consuming. Therefore, Lightweight-convolutional neural networks (LW-CNN) algorithm is proposed in this paper to achieve real-time fault diagnosis. The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method. Based on LW-CNN and data augmentation, the real-time intelligent diagnosis of co-frequency is realized. Moreover, a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis. It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.
Details
- Language :
- English
- ISSN :
- 21928258
- Volume :
- 37
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Chinese Journal of Mechanical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.3a012f617b4449a82a0228f6be8f0a
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s10033-024-01021-9