Abstract Motor-driven machines, such as water pumps, air compressors, and fans, are prone to fatigue failures after long operating hours, resulting in catastrophic breakdown. The failures are preceded by faults under which the machines continue to function, but with low efficiency. Most failures that occur frequently in the motor-driven machines are caused by rolling bearing faults, which could be detected by the noise and vibrations during operation. The incipient faults, however, are difficult to identify because of their low signal-to-noise ratio, vulnerability to external disturbances, and non-stationarity. The conventional Fourier spectrum is insufficient for analyzing the transient and non-stationary signals generated by these faults, and hence a novel approach based on wavelet packet decomposition and support vector machine is proposed to distinguish between various types of bearing faults. By using wavelet and statistical methods to extract the features of bearing faults based on time-frequency analysis, the proposed fault diagnosis procedure could identify ball bearing faults successfully.

中文翻译：

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球轴承元件故障诊断：基于时频分析的通用程序

摘要 水泵、空压机、风扇等电机驱动的机器在长时间运行后容易出现疲劳失效，从而导致灾难性的故障。故障之前是机器继续运行但效率低下的故障。电动机械中经常发生的故障大多数是由滚动轴承故障引起的，这可以通过运行过程中的噪音和振动来检测。然而，初期故障由于信噪比低、易受外部干扰和非平稳性而难以识别。传统的傅立叶频谱不足以分析由这些故障产生的瞬态和非平稳信号，因此提出了一种基于小波包分解和支持向量机的新方法来区分各种类型的轴承故障。通过使用小波和统计方法提取基于时频分析的轴承故障特征，所提出的故障诊断程序可以成功地识别出球轴承故障。