基于双时域微弱故障特征增强的轴承早期故障智能识别*

针对轴承早期微弱故障难以准确识别的问题，提出一种基于双时域微弱故障特征增强的轴承早期故障智能识别方法。利用广义S变换和Fourier逆变换推导出一种双时域变换，将轴承振动信号变换为双时域二维时间序列。根据双时域变换的能量分布特点，提取二维时间序列的主对角元素以构建故障特征增强的时域振动信号。仿真信号和轴承故障信号分析验证了双时域微弱故障特征增强的可行性和有效性。采用脉冲耦合神经网络和支持向量机对增强后的轴承信号进行时频特征参数提取和智能识别，平均识别精度达到了95.4%。试验结果表明所提方法能有效提高轴承早期故障的智能识别精度。

Rolling Bearing Early Fault Intelligence Recognition Based on Weak Fault Feature Enhancement in Time-Time Domain

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.