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Vol. 173, Issue 6, June 2014, pp. 158-165

 

Bullet

 

Study on AE in Mechanical Seal Lift-off Recognition of Mechanical Main Shaft
 

1 Erqing Zhang, 1 Pan Fu, 1 Zhendi Ge, 2 Zhi Zhang, 2 Zepei Huang

1 School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
2 Department of Science and Technology, Sichuan Sunny Seal Co., LTD, Chengdu, 610045, China
1 Tel.: 13880911913

E-mail: zrq@my.swjtu.edu.cn

 

Received: 30 May 2014 /Accepted: 27 June 2014 /Published: 30 June 2014

Digital Sensors and Sensor Sysstems

 

Abstract: For the problem of the determination of lift-off position and the measurement of end face thickness for mechanical seal more difficult, the method based on acoustic emission signal end face lift-off condition monitoring technology for mechanical seal was proposed. The electric eddy current sensor made direct measurement in the internal of mechanical seal device, and the acoustic emission sensor was fixed in the outside for indirect measurement. The acoustic emission signals were de-noised by wavelet threshold de-noising method. The representative energy features were selected by wavelet packet energy spectrum algorithm. It was established that the Radial Basis Function neural network model used for identification of the mechanical seal lift-off position, and the extracted wavelet energy features as its input. It was confirmed accurate and effective that the acoustic emission identification technology through comparing with the data detected by electric eddy current sensor. So using the acoustic emission technology realized the identification of the mechanical seal lift-off position of mechanical main shaft from inside to outside. It is convenient to be used and promotion in industrial field.

 

Keywords: Mechanical main shaft, Mechanical seal, Acoustic emission, Wavelet packet, Neural network.

 

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