Paper Title:
Application of Acoustic Emission on Fault Diagnosis of Rolling Element Bearing
  Abstract

Because AE (Acoustic Emission) signals in bearing fault monitoring unavoidably mixed various noise which lead to wide band characteristics, in this paper, the collected AE signals are pre-processed by EMD (Empirical Mode Decomposition) algorithm to extract useful information in the concerned frequency range, after that, power spectrum is used to locating analysis and pattern recognition. Experiment show that this method could improve the detection accuracy in rolling element bearing fault diagnosis.

  Info
Periodical
Advanced Materials Research (Volumes 199-200)
Edited by
Jianmin Zeng, Zhengyi Jiang, Taosen Li, Daoguo Yang and Yun-Hae Kim
Pages
895-898
DOI
10.4028/www.scientific.net/AMR.199-200.895
Citation
H. F. Yuan, P. Wang, H. Q. Wang, "Application of Acoustic Emission on Fault Diagnosis of Rolling Element Bearing", Advanced Materials Research, Vols. 199-200, pp. 895-898, 2011
Online since
February 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

  | Authors: Xue Jun Li, Zong Qun Deng, Ling Li Jiang, Kuan Fang He
Chapter 2: Sensors and Signal/Image Processing
Abstract:To solve the problem of difficult to extract fault features from the early weak AE signal, a method combining lifting wavelet with EMD is...
193
Authors: Yan Long Chen, Pei Lin Zhang
Chapter 1: Mechanical Science and Engineering
Abstract:While bearing fault signals are strongly interferenced by noise, diagnosis using EMD directly for bearings fault becomes incorrect. A scheme...
70
Authors: Ling Li Jiang, Bo Bo Li, Xue Jun Li
Chapter 3: Mechanical Transmission, Vibration and Noise
Abstract:Hilbert-Huang transform (HHT) is a very effective time-frequency analysis method, but it has some disadvantages. For example, the dense modal...
264
Authors: Xian You Zhong, Chun Hua Zhao, Hai Jiang Dong, Xian Ming Liu, Liang Cai Zeng
Chapter 4: Sensors, Measurement, Monitoring and Detection
Abstract:An approach of fault diagnosis of bearing based on empirical mode decomposition (EMD), sample entropy and 1.5 dimension spectrum was...
1027