In modern industry, motor failures are difficult to avoid. Traditional diagnosis methods are limited by feature extraction, and manual feature extraction can cause great variability. This paper proposes a diagnosis method based on vibration image and Convolutional Neural Network (CNN). By normalizing and binarizing the vibration data in one dimension, it forms the two-dimensional vibration images. Then, CNN completes the feature extraction and learning of image data, avoiding the limitations of traditional methods. A variety of common fault data can be generated by the motor fault state simulation of the experimental platform. After the test of experimental data, the proposed method can achieve 100% accuracy and realize error-free discrimination.
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