Intelligent fault diagnosis of rotating machinery based on improved hybrid dilated convolution network for unbalanced samples.

In practical industrial applications, obtaining a sufficient number fault samples for specific types of equipment fault can be challenging. As a result, there are frequently significantly fewer defect samples obtained than healthy samples, and the data samples that are obtained typically have a high noise level. To overcome these issues, this paper introduces a novel approach termed the improved hybrid dilated convolution network (HDCN) to address these limitations and enhance classification accuracy. The proposed method involves transforming the time domain vibration signal into a time-frequency domain image using short time fourier transform (STFT), enabling simultaneous extraction of frequency domain and time domain features. A multi-scale hybrid dilated convolution network is constructed to extract multiple scale fault features and identify characteristic information. Subsequently, an adaptive weight long short-term memory (LSTM) unit is designed to perform weighted fusion of multi-scale features. It can be amplifying the contribution of important features and minimizing the influence of non-relevant features. The scaled exponential linear unit (SELU) is utilized to mitigate the significant suppression of the activation function on a few class samples. Finally, the network model is simulated using the focal loss function to make it more suitable for the case where the fault samples are small and confusing. To assess the effectiveness of the suggested approach, extensive tests are carried out on simulated datasets as well as a public dataset.