Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of 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 is comprised of three processes: Firstly, a denoising filter is used to improve the signal -to -noise ratio; secondly, a correlation function is suggested to choose the most representative intrinsic mode functions (IMFs); and thirdly, a generalized Teager-Huang 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.

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