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Research Papers: SPECIAL SECTION PAPERS

Novel Defect Location Method for Pressure Vessel by Using L (0, 2) Mode Guided Wave

[+] Author and Article Information
Shuangmiao Zhai, Yong Li

School of Mechanical and Power Engineering,
East China University of Science
and Technology,
Shanghai 200237, China

Shaoping Zhou

School of Mechanical and Power Engineering,
East China University of Science
and Technology,
Shanghai 200237, China
e-mail: shpzhou@ecust.edu.cn

Shaojie Chen, Bin Yang

School of Mechanical and Power Engineering,
East China University of Science and
Technology,
Shanghai 200237, China

1Corresponding author.

Manuscript received November 9, 2017; final manuscript received February 28, 2018; published online December 14, 2018. Assoc. Editor: Fabrizio Paolacci.

J. Pressure Vessel Technol 141(1), 010910 (Dec 14, 2018) (10 pages) Paper No: PVT-17-1226; doi: 10.1115/1.4039502 History: Received November 09, 2017; Revised February 28, 2018

Pressure vessel plays an increasingly important role in process industries, in which its performance degradation, such as crack and corrosion, may lead to serious accidents and significant economic losses. Guided wave-based method is a cost-effective means for pressure vessel rapid interrogation. In this paper, the method based on direct-wave and fuzzy C-means clustering algorithm (FCM) is proposed to locate defect for pressure vessel. Finite element (FE) simulation is applied to analyze the propagation characteristics of guided waves. The experiment using the method based on direct-wave and FCM has been conducted on the barrel and head with different sensor arrays, respectively. The variation rule of the direct-wave difference with different distance coefficients has been studied. By combining FCM with the direct-wave difference, the defects on barrel and head can be detected accurately. The defect inspection experiment for pressure vessel using ellipse imaging algorithm is conducted as well. The experimental results show that the method based on direct-wave and FCM can locate the defects on barrel and head of the pressure vessel effectively and accurately.

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References

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Figures

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Fig. 1

Simulation model of pressure vessel

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Fig. 2

Excitation waveform

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Fig. 3

Guided waves propagation feature: (a) barrel and (b) head

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Fig. 4

Energy of guided waves attenuates with the increase of exciting time: (a) barrel and (b) head

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Fig. 5

Location relationship

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Fig. 6

Location algorithm

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Fig. 7

Clustering center by FCM

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Fig. 8

Principle of ellipse location: (a) two sensors and (b) three sensors

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Fig. 9

Experimental system

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Fig. 10

Excitation frequency selection of barrel: (a) dispersion curve, (b) changes in amplitudes with the depth of water, and (c) amplitude of three modes with different frequencies

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Fig. 11

Excitation frequency selection of barrel: (a) changes in amplitude with the frequencies from 100 kHz to 190 kHz and (b) time domain waveforms with frequencies from 160 kHz to 190 kHz

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Fig. 12

Sensor array of barrel

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Fig. 13

Time domain waveforms with different distance coefficients: (a) η = 1.0 and (b) η = 1.35

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Fig. 14

The effect of direct-wave difference with different distance coefficients

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Fig. 15

Location result of barrel

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Fig. 16

Sensor array of head

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Fig. 17

Location result of head: (a) received by sensor 5, (b) received by sensor 10, and (c) amplitude of all receiving sensors

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Fig. 18

Location result by ellipse imaging algorithm and data fusion method: (a) multimode characteristic and (b) location result

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