ArticleMicromachines2020
Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM.
Article in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Fault detection of high-speed train wheelset bearings based on improved auxiliary classifier generative adversarial networks and VAE.PloS one · 2025Article
- A Siamese Vision Transformer for Bearings Fault Diagnosis.Micromachines · 2022Article
- Complex-Valued Wavelet Spectrum Analysis of Respiratory Conditions and Its Feasibility in the Detection of Low-Functional Respiration.Healthcare (Basel, Switzerland) · 2021Article
- Editorial for the Special Issue on Micro-Manufacturing and Applications.Micromachines · 2021Article
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Authors and funding
5 authors.
Funding
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Abstract
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.
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Registered trials
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