Single and simultaneous fault diagnosis of gearbox via wavelet transform and improved deep residual network under imbalanced data

Playing a vital role in keeping gearbox working reliably and safely, smart fault diagnosis (FD) technology has attracted much attention in recent years. However, in practical industrial applications, owing to the imbalance of healthy state data and fault state data and various unpredictable compound fault modes, it is still extremely challenging to fulfill the high-accuracy and effective FD of gearbox based on existing intelligent diagnostic models. In this paper, a new method is proposed to tackle these problems by integrating an improved deep residual network (IDRN) and wavelet transform (WT). The proposed method mainly contains two parts: In the first part, the vibration signals are transformed into images by WT. In the second part, the IDRN is applied to realize an accurate FD of gearbox. Compared with the original deep residual network, the main differences of IDRN are as follows: first of all, a new loss function is used to replace the commonly used logistic loss function by adding a class-balanced re-weighting term and combining with multi-label classification. Then, attention modules focusing operations on specific regions and enhancing the features of some regions are combined with residual blocks. Finally, a spatial transformer module is inserted into the original deep residual network to scale images and clip the region of interest. Two trials are conducted to validate the effectiveness of WT-IDRN method. The experimental consequences demonstrate that the WT-IDRN method has more excellent performance than the existing intelligent FD method in accuracy and generalization ability.