摘要

A novel smart sensing unit is developed in this paper for vibration measurement and machinery condition monitoring. The microprocessor-based smart sensor can collect 2-D vibrations and conduct signal analysis. When mounted in proximity of a bearing housing (a general case), it can conduct online fault detection in shafts and bearings. A correlation spectrum method is proposed as a digital encoder to recognize shaft rotation speed. A wavelet energy spectrum technique is adopted for bearing fault detection. A novel strategy is suggested to extract representative features and enhance feature characteristics by integrating the resulting wavelet energy functions over different frequency bands. The effectiveness of the developed smart sensor and the related fault detection techniques is verified by experimental tests corresponding to different bearing conditions. Test results show that the developed smart sensing unit is an effective measurement and condition monitoring tool; the wavelet energy spectrum technique is a robust bearing fault detection method, especially for nonstationary feature extraction and analysis.

  • 出版日期2010-2