Bearing fault diagnosis is an important technique in industrial production as bearings are one of the key components in rotating machines. In bearing fault diagnosis, complex environmental noises will lead to inaccurate results. To address the problem, bearing fault classification methods should be capable of noise resistance and be more robust. In previous studies, researchers mainly focus on noise-free condition, measured signal and signal with simulated noise, many effective approaches have been proposed. But in real-world working condition, strong and complex noises are often leads to inaccurate results. According to the situation, this work focuses on bearing fault classification under the influence of factory noise and the white Gaussian noise. In order to eliminate the noise interference and take the possible connection between signal frames into consideration, this paper presents a new bearing fault classification method based on convolutional neural networks (CNNs). By using the sensitivity to impulse of spectral kurtosis (SK), noises are repressed by the proposed filtering approach based on the SK. Mel-frequency cepstral coefficients (MFCC) and delta cepstrum are extracted as the feature by the reason of satisfactory performance in sound recognition. And in consideration of the connection between frames, a feature arrangement method is presented to transfer feature vectors to feature images, so the advantages of the CNNs in the fields of image processing can be exploited in the proposed method. The proposed method is demonstrated to have strong ability of classification under the interference of factory noise and the Gaussian noise by experiments.INDEX TERMS Bearing fault, convolutional neural network, fault diagnosis, spectral kurtosis.

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