Abstract
In order to recognize the acoustic emission source with different characteristics, the parameter-ratio method was put forward to analyze the characteristic parameters of acoustic emission from different source further. According to the peak amplitude, counts, energy and rise-time, the three ratios of the amplitude to the energy difference, the amplitude to the counts difference and the amplitude to the rise-time difference were used as the parameter-ratios. Based on the matter-element of extension theory, a matter-element model was built to describe the characteristics of the acoustic emission. The dependent function and degree of the characteristics of the acoustic sources were introduced to evaluate the possibility of the acoustic sources. The acoustic sources can be recognized, putting forward the recognition rules of parameter-ratio method. The recognition example was taken to validate the parameter-ratio method. It is shown that the parameter-ratio method can recognize the acoustic emission source well.
Supported by the National Natural Science Foundation of China, No.50535010. Supported by the Chunhui Plan of Ministry of Education of China, No. z2005-1-21007.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tandon, N., Choudhury, A.: A Review of Vibration and Acoustic Measurement Methods for the Detection of Defects in Rolling Element Bearings. Tribology International 32, 469–480 (1999)
Miettinen, J., Andersson, P.: Acoustic Emission of Rolling Bearings Lubricated with Contaminated Grease. Tribology International 33, 743–802 (2000)
Huguet, S., Godin, N., Gaertner, R., Salmon, L., Villard, D.: Use of Acoustic Emission to Identify Damage Modes in Glass Fiber Reinforced Polyester. Composites Science and Technology 62, 1433–1444 (2002)
Toutountzakis, T., Tan, C.K., Mba, D.: Application of Acoustic Emission to Seeded Gear Fault Detection. NDT&E International 38, 27–36 (2005)
Steel, J.A., Reuben, R.L.: Recent Developments in Monitoring of Engines Using Acoustic Emission. Journal of Strain Analysis for Engineering Design 40, 45–57 (2005)
Jemielniak, K., Kwiatkowski, L., Wrzosek, P.: Diagnosis of Tool Wear Based on Cutting Forces and Acoustic Emission Measures as Inputs to a Neural Network. Journal of Intelligent Manufacturing 9, 447–455 (1998)
Yao, Y.X., Li, X., Yuan, Z.J.: Tool Wear Detection with Fuzzy Classification and Wavelet Fuzzy Neural Network. Int. J. Machine Tools & Manufacture 39, 1525–1538 (1999)
Yeo, S.H., Khoo, L.P., Neo, S.S.: Tool Condition Monitoring Using Reflectance of Chip Surface and Neural Network. Journal of Intelligent Manufacturing 11, 507–514 (2000)
Li, W.L., Tsai, Y.P., Chiu, C.L.: The Experimental Study of the Expert System for Diagnosing Unbalances by ANN and Acoustic Signals. Journal of Sound and Vibration 272, 69–83 (2004)
Tan, C.K., Mba, D.: Identification of the Acoustic Emission Source During a Comparative Study on Diagnosis of a Spur Gearbox. Tribol.Int. 38, 469–480 (2005)
Shen, G.T., Duan, Q.R., Zhou, Y.F., Li, B.X., Liu, Q.Z., Li, C.S., Jiang, S.L.: Investigation of Artificial Neural Network Pattern Recognition of Acoustic Emission Signals for Pressure Vessels. NDT 23, 144–146 (2001)
Shen, G.T., Zhou, Y.F., Duan, Q.R., Li, B.X.: Acoustic Emission Sources from Field Test of Pressure Vessels. NDT 21, 321–325 (1999)
Huang, Y.P., Chen, H.J.: The Integration of Extension Theory to Design a New Fuzzy Inference Model. International Journal on Intelligence Tools 9, 473–492 (2000)
Qin, J.J., Li, Q.: A Model of Intellective Fault Diagnosis on the Basis of Extension. In: ICNGC2001, pp. 357–360. Harbin Engineering University Press, Harbin (2001)
Cai, W.: Extension Management Engineering and Applications. International Journal of Operations and Quantitative Management 1, 59–72 (1999)
Shen, G.T., Geng, R.S., Liu, S.F.: Parameter Analysis of Acoustic Emission Signals. NDT 24, 72–77 (2002)
Carpinteri, A., Lacidogna, G., Pugno, N.: Time-scale Effects During Damage Evolution: A Fractal Approach Based on Acoustic Emission. Strength, Fracture and Complexity 3, 127–135 (2005)
Yao, L.: Distribution Analysis to Characteristic Parameter for Kinds of Typical AE Signals. NDT 28, 19–22 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Jin, W., Chen, C., Jin, Z., Gong, B., Wen, B. (2007). An Improved Recognition Approach of Acoustic Emission Sources Based on Matter Element. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_108
Download citation
DOI: https://doi.org/10.1007/978-3-540-74171-8_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74170-1
Online ISBN: 978-3-540-74171-8
eBook Packages: Computer ScienceComputer Science (R0)