Abstract: For the rolling bearing early weak fault diagnosis, a rolling bearing early fault intelligence recognition method based on weak fault feature enhancement in time-time domain is proposed. A novel time-time domain transform is derived from the generalized S transform and inverse Fourier transform. The time-time domain transform is utilized to convert bearing vibration signals to 2-D time series in time-time domain. According to the energy distribution of time-time domain transform, the leading diagonal elements of 2-D time series are selected for the construction of fault feature enhanced bearing vibration signals. Analysis of the simulation signal and bearing vibration signals validates the feasibility and effectiveness of weak fault feature enhancement in time-time domain. Time-frequency feature parameter extraction and intelligent recognition are then implemented on the enhanced bearing vibration signals by the pulse coupled neural network and support vector machine. As a result, the recognition accuracy reaches 95.4%. Experimental results indicate that the proposed method can effectively improve the intelligence recognition accuracy of rolling bearing early faults.

Key words: early fault diagnosis, pulse coupled neural network, time-time domain transform, rolling bearing