An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image

Deep learning theory has been widely used for diagnosing bearing faults. However, this method still has same drawbacks. For example, single time or frequency domain analysis methods cannot effectively extract features, the ReLU function is greatly affected by the learning rate, and it is difficult to achieve satisfactory results using the same regularization for different layers. To overcome the aforementioned deficiencies: (1) short-time Fourier transform theory to obtain an input image, (2) the scaled exponential linear unit (SELU) function is introduced to avoid excessive “dead” nodes during the training process, and (3) the use of hierarchical regularization to obtain better training results. Small sample datasets were used for the test experiment in two bearing fault simulators. The experiment results showed that the proposed method has a higher fault diagnosis accuracy than existing deep learning diagnosis methods.