LAUSR

Abstract The bearings fault diagnosis is essential for the maintenance and reliability of rotating machinery. Bearings pitting is one of the most common fault types of rotating machinery. However, due to the complex working conditions of bearings, it is challenging to diagnose the pitting faults in bearings inner and outer rings at different speeds. In this paper, an improved one-dimensional inception capsule network (IICN) is proposed to solve the problem of bearing pitting fault diagnosis under complex working conditions. Firstly, the raw bearing vibration signal is processed using the improved Inception network. The function of the stage is to approximate an optimal local sparse structure with a simple dense substructure for bearing healthy state feature extraction. And then inputs concatenated features to the primary capsule layer and the routing capsule layer. The inputs are mapped to feature vector space and weighted by the dynamic routing algorithm. The dynamic routing algorithm encodes the significant spatial relationship between low-level features and upper-level features. The Euclidean length of each capsule vector is the probability of belonging to this bearing healthy condition. In order to validate the effectiveness of the IICN method, bearings pitting experiments at different speeds were designed. The raw bearings vibration signal data under six different health conditions are collected, and the effectiveness of the IICN method is verified. Experimental results show that the IICN method can effectively distinguish different degrees of bearing pitting fault at different speeds, and its diagnostic accuracy is superior to other advanced deep learning methods.