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The Investigation of Artificial Neural Network Pattern Recognition of Acoustic Emission Signals for Pressure Vessel

Gongtian Shen, Qingru Duan, Bangxian Li and Qizhi Liu
(National Center of Boiler and Pressure Vessel Inspection and Research)
(China State Bureau of Quality and Technical Supervision)
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ABSTRACT:

1. INTRODUCTION

2. INVESTIGATION of BP NETWORK PATTERN RECOGNITION

3.INVESTIGATION OFRECOGNITION OF AE MECHANISM FOR PV

4. CONCLUSION

  1. Designed BP neural network can successfully distinguish the nature of field PV AE sources with the characteristic parameters of AE signals.
  2. A new quantitative analysis concept for AE sources of pressure vessel was introduced by using artificial neural network classification. A new method evaluating the severity of AE sources was raised. The designed and trained artificial neural network can give the percentage of crack growing, slag inclusion cracking, residual stress releasing and structure rubbing signals for an complex AE sources.
  3. The design of neural network structure and selection of training are very important factors affecting the recognition results. Using AE signals produced by as unitary as possible mechanism to train the network, the pattern recognition results will be better.

REFERENCES

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  2. Shifeng Liu,etal,"AE inspection and defect assessment for metal pressure vessels", International Journal of Pressure Vessel and Piping, 38, P57-67, 1989.
  3. T.J.Fowler, "Chemical Industry Applications of Acoustic Emission", Materials Evaluation, Vol.50, July, 1992: pp875-882.
  4. R.D.Tidswell, M.P.Shipley and B.J.Cane, "Development of new procedures for in-service acoustic emission testing in pressure vessels and pipework", Progress in Acoustic Emission , The Japanese Society for NDI, Japan, 1996.
  5. Gongtian Shen, Bangxian Li, Qingru Duan and Shifeng Liu: "Acoustic Emission Sources of Field Pressure Vessel Test", Progress in Acoustic Emission , The Japanese Society for NDI, Japan, 1996.
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  7. W. Schse and Igor Grabec, "Intelligent Processing of Acoustic Emission Signals", Journal of Materials Evaluation, Vol. 50, No.7, 1992: pp826-854.
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