| Summary: | <b> </b>In the prognostics health management (PHM) of rotating machinery, the accurate identification of bearing fault is critical. In recent years, various deep learning methods can well identify bearing fault based on monitoring data. However, facing changing operating conditions and noise pollution, the accuracy of these algorithms decreases significantly, which makes the algorithms difficult in practical applications. To solve this problem, a novel capsule network based on wide convolution and multi-scale convolution (WMSCCN) is proposed for fault diagnosis. The proposed WMSCCN algorithm takes one-dimensional vibration signal as an input and no additional manual processing is required. In addition, the adaptive batch normalization (AdaBN) algorithm is introduced to further enhance the adaptability of WMSCCN under noise pollution and load changes. On generalization experiments under different loads, the proposed WMSCCN and WMSCCN-AdaBN algorithms achieve average accuracy rates of 96.44% and 97.44%, respectively, which is superior to other advanced algorithms. In the noise resistance experiment, the proposed WMSCCN-AdaBN can maintain a 92.3% diagnostic accuracy in a strong noise environment with a signal to noise ratio (SNR) = −4 dB, showing a very strong anti-noise ability. When the SNR exceeds 4 dB, the accuracy reaches 100%, indicating that the proposed algorithm has a very good accuracy at low noise levels. Two experiments have effectively verified the validity and generalizability of the proposed model.
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