Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of the rotating machinery defects are related to bearing faults. However, reliable bearing fault detection still remains a challenging task, especially in industrial applications. The objective of this work is to develop a smart sensor-based monitoring system for vibration measurement and bearing fault detection. In this work, a smart sensor data acquisition (DAQ) system is developed for vibration signal measurement. A selective Teager-Huang transform (THT) technique is proposed for bearing fault detection; it takes three processes: Firstly, a denoising filter is used to improve the signal to noise ratio; secondly, a correlation function is suggested to choose most representative intrinsic mode functions (IMFs); and thirdly, a generalized Teager-Kaiser spectrum method is proposed to process the extracted IMFs for bearing fault detection. The effectiveness of the developed DAQ system and selective THT technique is verified by experimental tests.

中文翻译：

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一种用于振动测量和轴承故障检测的基于传感器的智能监测系统

滚动轴承常用于旋转机械和电气设备。据调查，一半以上的旋转机械缺陷与轴承故障有关。然而，可靠的轴承故障检测仍然是一项具有挑战性的任务，尤其是在工业应用中。这项工作的目标是开发一种基于智能传感器的监测系统，用于振动测量和轴承故障检测。在这项工作中，开发了一种用于振动信号测量的智能传感器数据采集 (DAQ) 系统。提出了一种选择性的Teager-Huang变换(THT)技术用于轴承故障检测；它需要三个过程：首先，使用去噪滤波器来提高信噪比；其次，建议使用相关函数来选择最具代表性的本征模式函数（IMF）；第三，提出了一种广义的Teager-Kaiser谱方法来处理提取的IMF用于轴承故障检测。通过实验测试验证了开发的 DAQ 系统和选择性 THT 技术的有效性。