Ultrasonic Detection And Recognition Of Weld Flaw Based On Multi-feature And Data Fusion

With the development of modern industry, welded structures have been widelyused in boiler, pressure vessel, rail, marine, shipbuilding, energy, aerospace and otherindustrial fields. At present, conventional manual ultrasonic testing was widely usedto detect the welded structure in service. However, conventional ultrasonic testingmethod cannot acquire the continuous echoes of flaw, and the data cannot be storedautomatically. Thus this method is inefficient, and the flaw is easy to be missedduring the detection process. Furthermore, flaw recognition is still a difficult problemto be resolved in conventional ultrasonic testing method. The three-view projectionimage of flaw cannot be illustrated automatically and intuitively. So the recognitionand diagnosis of flaw completely relies on the experience of the operator, whichmakes the detection result vary from person to person. Thus, there are many issuesfor detecting welded structures in service by conventional ultrasonic testing.To solve the problems which exist in conventional ultrasonic testing, a manualultrasonic testing system based on video image positioning was developed in thispaper. This system not only has many characters of cheap cost, simple structure andconvenient to carry, but also can achieve a continuous scanning of weld flaw, theautomatic storage of data and the three-view projection imaging of weld flaw, whichprovides the reliable data for the intelligent recognition of weld flaw.There are many butt weld specimens, which contain five types of typical weldflaws of porosity, slag, crack, lack of penetration and lack of fusion. They wereinspected by this developed system, and ultrasonic echoes reflected from each defectwere stored in real time. Then the three-view projection image of weld flaw wasshowed, and the location, size and distribution of weld flaw were characterizedconveniently and intuitively, which provides a reliable data source for featureextraction of weld flaw. For intelligent recognition of defect by using artificialintelligence method, the most critical thing is to obtain the feature which can reflectthe characteristic of different defects. By analyzing the characteristics of differentflaw reflectors, flaw features were extracted in time domain, frequency domain andtime-frequency domain. Furthermore, geometric features, statistical features andmorphological features were also extracted. Thus, the flaw features were extracted inmulti-domain in this paper.Flaw feature plays an important role in intelligent recognition of weld flaw.Correct selection of efficient feature vector is necessary to ensure a goodperformance of classification for the recognition system. Thus, evaluation criterion based on Euclidean distance was constructed to evaluate and optimize the feature.Then the optimum feature subset was obtained, and the dimensionality reduction offeature space was brought out. The results provided efficient feature vectors for theflaw recognition.Intelligent recognition classifier was constructed base on back propagation (BP)neural network. Then a new method of flaw recognition based on multi-feature ofultrasonic signal feature and morphological feature was studied. And it was applied toidentify the five types of weld flaws. This method made full use of the morphologicalinformation of weld flaw. Thus, compared to the conventional recognition resultbased on ultrasonic signal feature, the recognition rate of weld flaws was improvedeffectively.Based on Dempster-Shafer (D-S) evidence theory, a new method of flawrecognition based on data fusion of dual-probe sensor was studied. More accurate andcomprehensive flaw information was obtained by the data fusion of dual-probeinformation. Then an intelligent pattern classifier based on BP neural network andD-S evidence theory was developed to carry out the flaw recognition of five types ofweld flaws. This method combined the complementary information of dual-probesensor, improved the utilization of defect information, and reduced the systematicuncertainty of single probe. Thus, compared to the recognition result based on singleprobe, the reliability and accuracy of flaw recognition was improved effectively.