A fully automatic bearing fault diagnosis method based on an improved polar coordinate image texture

Previous researchers’ Bearing Fault Diagnosis (BFD) methods often employ signal processing techniques to handle one-dimensional vibration signals, enabling the emergence of recognizable Bearing Fault Features (BFFs) in the Cartesian coordinate system. However, due to noise interference or limited by the manifestation of BFFs, these BFFs often require highly specialized personnel to identify and extract them, making the achievement of fully automated bearing fault diagnosis extremely challenging. Hence, a fully automatic BFD method based on Improved Polar Coordinate (IPC) image texture is proposed. Firstly, the proposed IPC algorithm transforms vibration signals into IPC images with easily recognizable BFFs in the polar coordinate system. Then, automatic image filtering, image texture enhancement, and texture feature extraction are achieved through methods in the field of image processing. Finally, automatic BFD experiments are conducted using extracted IPC image texture features and a neural network. The entire BFD process is fully automatic, and the methods employed are relatively simple and easy to implement, which is highly advantageous for promoting and implementing a real-time fault monitoring system. Experimental results show that the proposed fully automated BFD method based on IPC image texture is effective, achieving an average diagnostic accuracy of 99.4%. This surpasses the 95.0% accuracy of a similar method based on symmetrical polar coordinate image texture and the 98.9% accuracy of an advanced method based on refined composite multi-scale dispersion entropy. Moreover, the proposed method also has significant advantages in diagnosis efficiency compared to the advanced method.