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Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram
| Content Provider | MDPI |
|---|---|
| Author | Pham, Minh Tuan Kim, Jong-Myon Kim, Cheol Hong |
| Copyright Year | 2020 |
| Description | Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful information from vibration signals, diagnosis of motor failures by maintenance engineers can be gradually replaced by an automatic detection process. Especially, state-of-the-art methods using deep learning have contributed significantly to automatic fault diagnosis. This paper proposes a novel method for diagnosing bearing faults and their degradation level under variable shaft speed. In the proposed method, vibration signals are represented by spectrograms to apply deep learning methods through preprocessing using Short-Time Fourier Transform (STFT). Then, feature extraction and health status classification are performed by a convolutional neural network (CNN), VGG16. According to our various experiments, our proposed method can achieve very high accuracy and robustness for bearing fault diagnosis even under noisy environments. |
| Starting Page | 6385 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10186385 |
| Journal | Applied Sciences |
| Issue Number | 18 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-09-13 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Fault Diagnosis Bearing Fault Machine Health Monitoring Vibration Signals Spectrogram Convolutional Neural Network |
| Content Type | Text |
| Resource Type | Article |
