Motor Running State Detection by Dropout-CNN Based on NLWT Coefficient Enhancement
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    Abstract:

    To identify the running fault of motors quickly and effectively from the temperature data collected by thermal imagers, this study combines dropout, nonlinear wavelet transform coefficient enhancement (NLWTCE), and convolutional neural network (CNN) algorithm to identify the motor image. Firstly, the image dataset of the motor is established according to the data collected by the thermal imager and the data image is enhanced by nonlinear wavelet transform (NLWT). Then an improved CNN (ICNN) model is built to identify the image with the extracted features as the final recognition features. Finally, compared with the normal motor images, the faulty motor images are effectively and accurately identified. The experimental results show that the ICNN model not only has a high recognition accuracy but also further simplifies the complex extraction of image features. The validity and reasonableness of the method are verified, and the method is suitable for engineering application.

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龙慧,马家庆,吴钦木,何志琴,陈昌盛,覃涛.基于NLWT系数增强的随机失活CNN电机运行状态检测.计算机系统应用,2023,32(3):345-351

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History
  • Received:August 22,2022
  • Revised:September 22,2022
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  • Online: November 29,2022
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