·Table of Contents
·Computer Processing and Simulation
The Investigation of Artificial Neural Network Pattern Recognition of Acoustic Emission Signals for Pressure VesselGongtian 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)
Keywords: pressure vessel, acoustic emission, artificial neural network, pattern recognition.
However, it is very difficult to perform normal NDT verification for the fillet of nozzle, lad, skirt and support plates of saddles of PV. It is not able to perform normal NDT for on-line PV or PV with thermal insulation. These factors led can not make safety evaluation for PV. The use of AE test is restricted.
In recent years, a few papers report that the natures of some AE sources can be distinguished by artificial neural network pattern recognition of AE waveform[6-8].But most of AE instruments only can acquire and save AE characteristic parameters in the field pressure vessel test. This paper try to use the artificial neural network pattern recognition technique analyzing AE characteristic parameter to distinguish the characters of AE sources.
|Fig 1: The structure of a 5X5X3 neural network||Fig 2: The principle of error back propagation|
According to the requirement of AE test for field PV and the principle of convenient operating on personnel computer, a 50X50X5 BP neural network was designed. The 5 output patterns are classified as welding surface crack, welding inner crack, welding defects, residual stress releasing and mechanical hit or friction. The network was trained by 5 typical AE source signals that every one has approximate 500 AE hits. After 400 times training, the average squire error is 0.14 and the average correct recognition ratio is 93%. Table 1 lists the classification results of pattern recognition for training AE data and test AE data. The lowest correct recognition ratio is 86.5% and 84% respectively for training AE data of welding defects and test AE data of surface crack and welding defect. These results illustrate that the training effect of the network is very good. The trained network is possessed of very high generalizing capacity.
|Table 1: The training and test results of network for 5 typical AE sources (Classification ratio %)|
Table 2 lists the pattern recognition analyzing results performed by the trained network for 6 field PV AE sources listed in table 3. The recognition results of 5 AE sources in table 2 are basically in accordance with the normal NDT results in table 3. The total correct recognition ratio is 83%. Only the second AE source which is from welding scar surface crack is wrongly distinguished as mechanical friction. Through analysis the reasonable explanation was got as follows. Due to the biggest depth of the surface crack is 3mm and the thickness of the shell is 34mm, the surface crack can not grow under 2.0MPa pressure. But the AE data training network was produced by surface crack growing, so the patterns of these two AE sources is indeed different. In addition, the classification result of pattern recognition shows that the AE signals from welding scar surface cracks are produced by friction between crack faces.
|No.1 AE source||9.8||82.5||5.4||2.3||0|
|No.2 AE source||0||0||0||0||100|
|No.3 AE source||10.8||0||83.8||5.4||0|
|No.4 AE source||0||0||0||0||100|
|No.5 AE source||0||0||25.2||13.0||61.8|
|No.6 AE source||0||0||39.6||25.5||34.5|
|Table 2: Recognition results of 6 field PV AE sources listed in table 3. (Classification ratio %)|
|No.||Description of AE sources||NDT verified results|
|1||Located on a longitudinal weld of a 1000m3 liquid petroleum gas (LPG) sphere.||One inner crack under surface 5mm with 15mm long and 10mm high.|
|2||Located on a welding scar of a 1000m3 LPG sphere.||3 surface cracks with respectively 15mm, 20mm and 30mm long, The highest depth is 3mm.|
|3||Located on a longitudinal weld of a 400m3 LPG sphere.||A lot of porosity, slag inclusion, incomplete fusion and incomplete penetration.|
|4||Spread on the fillet between shell and skirt of a heat exchanger.||No defect|
|5||Located on the fillet between shell and support lag of a 120m3 argon gas sphere.||A shallow surface crack with 10mm long and 0.5mm deep.|
|6||Located on a insulation support ring of a hydrogen cylinder.||The ring had been seriously corroded. There are a lot of oxides between ring and shell of cylinder.|
|Table 3: AE sources and NDT verified results of field PV|
From table 2 we can find another significant result that the mechanism of AE sources can be quantitatively analyzed by artificial neural network. Due to training AE data of surface crack include AE signals produced by cracking of slag inclusion and the training AE data of welding defects include AE signals from residual stress releasing, the analysis performed by this network can not give accurate result of AE signals produced by different mechanism. In next section a new network was designed to analyze the mechanism producing AE signals.
Table 4 lists the pattern recognition results of 8 AE sources of field PV performed by the trained network. The results show that artificial neural network pattern recognition can quantitatively analyze the mechanism of AE sources. The most significant finding is that the crack growing behavior of AE sources can be distinguished and the dangerous degree of AE sources can be quantitatively evaluated. For example, No.3 AE source is more dangerous than No. 8 although they are all welding defects. Even there are some surface cracks in No.2, but it is safety because the cracks did not grow. The recognition results of AE sources located on skirt fillet and lag fillet are satisfactory. The mechanisms produced AE signals include mechanical friction, oxides cracking and residual stress releasing.
|Output||Crack growing||Oxides cracking and peeling off||Residual stress releasing||Mechanical friction|
|1. Inner crack||94.5||0.5||4.3||0.7|
|2. Welding scar surface crack||0||97.7||0||2.3|
|3. Welding defect 1||17.9||2.8||76.2||3.1|
|4. Skirt fillet||0.1||23.2||0||76.7|
|5. Lag fillet||0||20.6||32.4||47|
|6. Support ring||0.1||20.1||76.1||3.7|
|7. Surface crack||57.2||1.0||9.5||32.3|
|8. Welding defect 2||0.6||2.8||96.1||0.5|
|Table 4: Recognition results of 8 field PV AE sources (Classification ratio %)|
However, still there are some problems for the recognition results of this network. The AE signals produced by oxides cracking and residual stress releasing are easily confused. It is incorrect that 76% AE signals of insulation support ring was classified as residual stress releasing signals. The reason is that the training AE data were not produced by single mechanism.
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