An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis

Since the continuous shrinking of the spatial distance between components from large-scale mechanized equipment which causes the physical characteristic signals transmitted by them to be buried in a large amount of interference noise. Aiming at the poor anti-noise performance and high computation complexity of conventional fault diagnosis methods toward rotatory machinery. This study proposes a one-dimension convolutional neural network (1DCNN) for fault diagnosis that directly worked on time-domain signals. A compact 1DCNN network structure is deeply optimized under the Pytorch software environment; features are automatically extracted from the background noise. Moreover, fully connected neural networks, softmax activation function, Adam optimization algorithm, and cross-entropy loss function are utilized for achieving the accuracy of over 99% to multiclassification tasks. Subsequently, when dealing with the noise-rich scenario, simulation results demonstrate that the accuracy rate can reach 98.31% when the signal-to-noise ratio is -8dB. What's more, the proposed model can still keep the valuable accuracy rate at 87.27% under -10 dB, which made a breakthrough contribution to the model's anti-noise performance by more than 7%. The result of this work verifies the effectiveness of the anti-noise robustness performance, thereby becoming a cornerstone inside the preventive fault diagnosis system for rotatory machinery.