Rotating Electrical Machines Defect Signal Measurement and Analysis Using Acoustic Emission Method

• Main Authors: Ling-ChihWei, 魏伶芷
• Other Authors: Cheng-Chi Tai
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/mesc7f

碩士 === 國立成功大學 === 電機工程學系 === 102 === In this thesis, an acoustic emission (AE) sensor was used to capture signals from defects in rotating electrical machines. The captured signals are often blurred by noises; therefore, to identify the defect signals, they are differentiated from noise by using wavelet analysis (WA) methods. The signal processing system developed in this study incorporates two types of wavelet analysis methods: the wavelet packet method and wavelet denoise method. In addition, fast fourier transform (FFT) is adopted to obtain the frequency distribution of defect signal data, and the defect signals are displayed in 3D space by using PD patterns. An identification system that can distinguish the defective parts of rotating electrical machines is constructed. The effectiveness of the system is analyzed by comparing the experimentally measured defect signal data from three defective parts of rotating electrical machine (e.g., the rotor, stator and shaft). The consistency of the analysis results is verified by conducting experiments on several rotating electrical machines.