Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM
To solve the problem of vibration motor fault detection accuracy and inefficiency in smartphone components, this paper proposes a fault diagnosis method based on the wavelet packet and improves long and short-term memory network. First, the voltage signal of the vibration motor is decomposed by a wa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-07-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/11/8/753 |
| _version_ | 1797560624806363136 |
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| author | Ruirui Wang Zhan Feng Sisi Huang Xia Fang Jie Wang |
| author_facet | Ruirui Wang Zhan Feng Sisi Huang Xia Fang Jie Wang |
| author_sort | Ruirui Wang |
| collection | DOAJ |
| description | To solve the problem of vibration motor fault detection accuracy and inefficiency in smartphone components, this paper proposes a fault diagnosis method based on the wavelet packet and improves long and short-term memory network. First, the voltage signal of the vibration motor is decomposed by a wavelet packet to reconstruct the signal. Secondly, the reconstructed signal is input into the improved three-layer LSTM network as a feature vector. The memory characteristics of the LSTM network are used to fully learn the time-series fault feature information in the unsteady state signal, and then, the model is used to diagnose the motor fault. Finally, the feasibility of the proposed method is verified through experiments and can be applied to engineering practice. Compared with the existing motor fault diagnosis method, the improved WP-LSTM diagnosis method has a better diagnosis effect and improves fault diagnosis. |
| first_indexed | 2024-03-10T18:03:14Z |
| format | Article |
| id | doaj.art-83ef7522df634d259c0b1629b36a5dee |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T18:03:14Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-83ef7522df634d259c0b1629b36a5dee2023-11-20T08:42:22ZengMDPI AGMicromachines2072-666X2020-07-0111875310.3390/mi11080753Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTMRuirui Wang0Zhan Feng1Sisi Huang2Xia Fang3Jie Wang4School of Mechanical Engineering, Sichuan University, Chengdu 610041, ChinaSchool of Mechanical Engineering, Sichuan University, Chengdu 610041, ChinaSchool of Mechanical Engineering, Sichuan University, Chengdu 610041, ChinaSchool of Mechanical Engineering, Sichuan University, Chengdu 610041, ChinaSchool of Mechanical Engineering, Sichuan University, Chengdu 610041, ChinaTo solve the problem of vibration motor fault detection accuracy and inefficiency in smartphone components, this paper proposes a fault diagnosis method based on the wavelet packet and improves long and short-term memory network. First, the voltage signal of the vibration motor is decomposed by a wavelet packet to reconstruct the signal. Secondly, the reconstructed signal is input into the improved three-layer LSTM network as a feature vector. The memory characteristics of the LSTM network are used to fully learn the time-series fault feature information in the unsteady state signal, and then, the model is used to diagnose the motor fault. Finally, the feasibility of the proposed method is verified through experiments and can be applied to engineering practice. Compared with the existing motor fault diagnosis method, the improved WP-LSTM diagnosis method has a better diagnosis effect and improves fault diagnosis.https://www.mdpi.com/2072-666X/11/8/753fault detectionminiature vibration motorwavelet packetLSTM neural network |
| spellingShingle | Ruirui Wang Zhan Feng Sisi Huang Xia Fang Jie Wang Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM Micromachines fault detection miniature vibration motor wavelet packet LSTM neural network |
| title | Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM |
| title_full | Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM |
| title_fullStr | Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM |
| title_full_unstemmed | Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM |
| title_short | Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM |
| title_sort | research on voltage waveform fault detection of miniature vibration motor based on improved wp lstm |
| topic | fault detection miniature vibration motor wavelet packet LSTM neural network |
| url | https://www.mdpi.com/2072-666X/11/8/753 |
| work_keys_str_mv | AT ruiruiwang researchonvoltagewaveformfaultdetectionofminiaturevibrationmotorbasedonimprovedwplstm AT zhanfeng researchonvoltagewaveformfaultdetectionofminiaturevibrationmotorbasedonimprovedwplstm AT sisihuang researchonvoltagewaveformfaultdetectionofminiaturevibrationmotorbasedonimprovedwplstm AT xiafang researchonvoltagewaveformfaultdetectionofminiaturevibrationmotorbasedonimprovedwplstm AT jiewang researchonvoltagewaveformfaultdetectionofminiaturevibrationmotorbasedonimprovedwplstm |