A novel collaborative bearing fault diagnosis method based on multi-scale dynamic fusion network under speed fluctuating condition

Abstract Improving bearing fault diagnosis accuracy under speed fluctuation is a challenge in engineering applications. With the development of big data processing technology, a new solution, multi-sensor complementary information, has emerged. However, single-scale dimension compression, which is adopted in most multi-sensor data fusion methods, captures only a small amount of valuable information. To deal with this deficiency, a multi-scale dynamic fusion network (MSDFN) is proposed. First, considering the existence of non-stationary features in the fluctuating speed signal, the FReLU function is adopted to activate the features after considering contextual information. Then, multi-sensor features are fused by multiple scales to obtain richer feature information, and fusion features at different scales are weighted by using the attention mechanism. Finally, batch normalization is employed to standardize the variable speed feature distribution. The validity of the MSDFN is proved by conducting fault diagnosis experiments on two bearings under speed fluctuating conditions. Experimental results indicate that the MSDFN is not only effective in identifying various types of fault samples, but also shows higher stability in multiple trials when compared with other methods.