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210 / 2023-10-27 22:47:01
Electric Motor Bearing Fault Noise Detection with Mel-Transformer Model and Multi-Timescale Feature Extraction
electric motor,bearing,Artificial Intelligence,feature extraction
7.Electric Machine Design and Control > (3)、Other topics in electric machine
Final Paper
Xiaotian Zhang / UoY
Yunshu Liu / The Chinese University of Hong Kong
Chao Gong / Northwestern Polytechnical University
Yu Nie / University of york
Jose Rodriguez / Universidad Andres Bello
Bearings are often found in various industrial systems such as electric motors, and their failure often results in industrial losses and personal danger. This paper proposes a transformer-based method to classify the bearing noise signal efficiently and accurately. Firstly, the vibration noise sample signals are used to be extract the fault feature information by multi-timescale Mel-spectrograms. Secondly, this paper proposes Mel-transformer architecture which is the first to apply the vision Transformer-based algorithm model to fault detection task. This method has a powerful ability to automatically extract audio feature information from the Mel-spectrogram feature map and can distinguish various fault types effectively. Compared with convolutional neural network (CNN) based model, the proposed method is more suitable for processing large-scale bearing data in the industry and requires lower computing resources. It also overcomes the problem that the model cannot be processed in parallel on the vibration sequence due to the limitation of the RNN structure. The effectiveness and feasibility of the proposed method are verified by CWRU dataset.
Important Date

  • Conference Date

    Dec 08

    2023

    to

    Dec 10

    2023

  • Nov 01 2023

    Draft paper submission deadline

  • Dec 10 2023

    Registration deadline

Sponsored By
IEEE IAS
Organized By
Southwest Jiaotong University (SWJTU)
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