Journal of Materials Engineering and Performance

, Volume 26, Issue 4, pp 1784–1791 | Cite as

An Integrated Processing Method for Fatigue Damage Identification in a Steel Structure Based on Acoustic Emission Signals

  • Yubo Zhang
  • Hongyun Luo
  • Junrong Li
  • Jinlong Lv
  • Zheng Zhang
  • Yue Ma


This paper presents an integrated processing method that applies principal component analysis (PCA), artificial neural network (ANN), information entropy and information fusion technique to analyze acoustic emission signals for identifying fatigue damage in a steel structure. Firstly, PCA is used to build different data spaces based on the damage patterns. Input information from each sensor is diagnosed locally through ANN in the data space. The output of the ANNs is used for basic probability assignment. Secondly, the first fusion operation adopts Dempster-Shafer (D-S) evidence theory to combine the basic probability assignment value of ANNs in the different data space of a sensor. Finally, the fusion results of each sensor are combined by D-S evidence theory for the second fusion operation. In this paper, information entropy is used to calculate the uncertainty and construct basic probability assignment function. The damage identification method is verified through four-point bending fatigue tests of Q345 steel. Validation results show that the damage identification method can reduce the uncertainty of the system and has a certain extent of fault tolerance. Compared with ANN and ANN combined with information fusion methods, the proposed method shows a higher fatigue damage identification accuracy and is a potential for fatigue damage identification.


acoustic emission construction damage identification steel 



This work was financially supported by National Key Technology R&D Program of China (2015BAG20B04 and 2015BAF06B01-3), the National Key Research and Development Program of China (2016YFC0801903) and National Natural Science Foundation of China (Nos. 51175023 and U1537212).


  1. 1.
    T. Roberts and M. Talebzadeh, Acoustic Emission Monitoring of Fatigue Crack Propagation, J. Constr. Steel Res., 2003, 59, p 695–712CrossRefGoogle Scholar
  2. 2.
    M. Arrington, Acoustic Emission-A Review, Non-Destructive testing, 1989, p 429–434Google Scholar
  3. 3.
    T. Jayakumar, C. Mukhopadhyay, S. Venugopal, S. Mannan, and B. Raj, A Review of the Application of Acoustic Emission Techniques for Monitoring Forming and Grinding Processes, J. Mater. Process. Technol., 2005, 159, p 48–61CrossRefGoogle Scholar
  4. 4.
    D. Aggelis, E. Kordatos, and T. Matikas, Acoustic Emission for Fatigue Damage Characterization in Metal Plates, Mech. Res. Commun., 2011, 38, p 106–110CrossRefGoogle Scholar
  5. 5.
    A. Fallahi, R. Khamedi, G. Minak, and A. Zucchelli, Monitoring of the Deformation and Fracture Process of Dual Phase Steels Employing Acoustic Emission Techniques, Mater. Sci. Eng. A, 2012, 548, p 183–188CrossRefGoogle Scholar
  6. 6.
    I. Daniel, J.-J. Luo, C. Sifniotopoulos, and H.-J. Chun, Acoustic Emission Monitoring of Fatigue Damage in Metals, Nondestructive Testing and Evaluation, 1998, 14, p 71–87CrossRefGoogle Scholar
  7. 7.
    K.-W. Nam, C.-Y. Kang, J.-Y. Do, S.-H. Ahn, and S.-K. Lee, Fatigue Crack Propagation of Super Duplex Stainless Steel with Dispersed Structure and Time-Frequency Analysis of Acoustic Emission, Met. Mater. Int., 2001, 7, p 227–231CrossRefGoogle Scholar
  8. 8.
    R. Piotrkowski, E. Castro, and A. Gallego, Wavelet Power, Entropy and Bispectrum Applied to AE Signals for Damage Identification and Evaluation of Corroded Galvanized Steel, Mech. Syst. Signal Process., 2009, 23, p 432–445CrossRefGoogle Scholar
  9. 9.
    M.J. Eaton, R. Pullin, J.J. Hensman, K.M. Holford, K. Worden, and S.L. Evans, Principal Component Analysis of Acoustic Emission Signals From Landing Gear Components: An Aid to Fatigue Fracture Detection, Strain, 2011, 47, p E588–E594CrossRefGoogle Scholar
  10. 10.
    K.M. Holford, R. Pullin, S.L. Evans, M.J. Eaton, J. Hensman, and K. Worden, Acoustic Emission for Monitoring Aircraft Structures, Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 2009, 223, p 525–532CrossRefGoogle Scholar
  11. 11.
    S. Rippengill, K. Worden, K.M. Holford, and R. Pullin, Automatic Classification of Acoustic Emission Patterns, Strain, 2003, 39, p 31–41CrossRefGoogle Scholar
  12. 12.
    J. Taghizadeh and M. Ahmadi, Identification of Damage Modes in Polypropylene/Epoxy Composites by Using Principal Component Analysis on AE Signals Extracted from Mode I, Delamination, Nondestruct. Test. Eval., 2012, 27, p 151–170CrossRefGoogle Scholar
  13. 13.
    D.D.L. Hall, S.A.H. McMullen, Mathematical Techniques in Multisensor Data Fusion 2nd ed., Artech House Publishers, Norwood, 2004Google Scholar
  14. 14.
    O. Basir and X. Yuan, Engine Fault Diagnosis Based on Multi-Sensor Information Fusion Using Dempster–Shafer Evidence Theory, Inf. Fusion, 2007, 8, p 379–386CrossRefGoogle Scholar
  15. 15.
    B.S. Yang and K.J. Kim, Application of Dempster–Shafer Theory in Fault Diagnosis of Induction Motors Using Vibration and Current Signals, Mech. Syst. Signal Process., 2006, 20, p 403–420CrossRefGoogle Scholar
  16. 16.
    Z. Han, H. Luo, J. Cao, and H. Wang, Acoustic Emission During Fatigue Crack Propagation in a Micro-Alloyed Steel and Welds, Mater. Sci. Eng. A, 2011, 528, p 7751–7756CrossRefGoogle Scholar
  17. 17.
    H. Wang, H. Luo, Z. Han, and Q. Zhong, Investigation of Damage Identification of 16Mn Steel Based on Artificial Neural Networks in Tensile Test, Reliability, Maintainability and Safety, 2009, 8th International Conference on ICRMS 2009, IEEE, 2009, p 1057–1061Google Scholar
  18. 18.
    H. Wang, H. Luo, Z. Han, and Q. Zhong, Investigation of Damage Identification of 16Mn Steel Based on Artificial Neural Networks and Data Fusion Techniques in Tensile Test, Advanced Data Mining and Applications, 2009, p 696–703Google Scholar

Copyright information

© ASM International 2017

Authors and Affiliations

  • Yubo Zhang
    • 1
    • 2
    • 3
    • 4
  • Hongyun Luo
    • 1
    • 2
    • 3
  • Junrong Li
    • 1
  • Jinlong Lv
    • 5
  • Zheng Zhang
    • 1
  • Yue Ma
    • 1
  1. 1.Key Laboratory of Aerospace Materials and Performance (Ministry of Education), School of Materials Science and EngineeringBeijing University of Aeronautics and AstronauticsBeijingPeople’s Republic of China
  2. 2.The Collaborative Innovation Center for Advanced Aero-Engine (CICAAE)Beijing University of Aeronautics and AstronauticsBeijingPeople’s Republic of China
  3. 3.Beijing Key Laboratory of Advanced Nuclear Materials and PhysicsBeijing University of Aeronautics and AstronauticsBeijingPeople’s Republic of China
  4. 4.China Waterborne Transport Research InstituteBeijingChina
  5. 5.Beijing Key Laboratory of Fine Ceramics, Institute of Nuclear and New Energy TechnologyTsinghua UniversityBeijingChina

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