DEFECT IDENTIFICATION BY AN ULTRASONIC CYLINDRICAL PHASED ARRAY
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- 英文论文题名：DEFECT IDENTIFICATION BY AN ULTRASONIC CYLINDRICAL PHASED ARRAY
- 论文作者：Shi-wen CHEN ; Bi-xing ZHANG
- 英文论文作者：Shi-wen CHEN ; Bi-xing ZHANG ; State Key Laboratory of Acoustics ; Institute of Acoustics ; Chinese Academy of Sciences
- 年：2016
- 作者机构：State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences;
- 英文论文关键词：Ultrasonic cylindrical phased array ; Defect classification ; Wavelet-packet transform ; Deep neural network
- 会议召开时间：2016-10-21
- 会议录名称：2016年全国压电和声波理论及器件应用研讨会论文摘要集
- 英文会议录名称：Abstract Book of 2016 Symposium on Piezoelectricity, Acoustic Waves and Device Applications
- 语种：英文
- 分类号：TB55
- 学会代码：AGLU
- 会议名称：2016年全国压电和声波理论及器件应用研讨会
- 会议地点：中国陕西西安
- 主办单位：中国力学学会、中国声学学会、IEEE UFFC分会
- 学会名称：中国力学学会
- 页数：2
- 文件大小：46k
- 原文格式：D
- 会议级别：全国
摘要
Background, Motivation and Objective Ultrasonic phased array technology is widely used in industrial Non-Destructive Testing(NDT). However, the traditional planar phased array transducer is usually used to scan the object with flat surface. To imaging the inner surface of a borehole wall, an ultrasonic cylindrical phased array transducer is presented to fulfill the whole scanning process for the inner surface. On the other hand, conventional defects identification usually depends on the specific professional knowledge and the experience of the operating personnel which could cause the instability and individual differences of the analysis results easily. In this paper, a new method is presented and studied for defect detection and classification with the cylindrical phased array by the wavelet-packet transform(WPT) and deep neural network. Statement of Contribution/Methods The WPT is used to extract the signal feature of the ultrasonic echo from defects, and the deep neural network is used to classify the defects feature. Firstly, a finite element model is conducted to simulate the defects identification by the cylindrical phased array transducer. A series of simulation are done for two types of defects with different sizes by a 64-element cylindrical phased array transducer with the center frequency of 500 k Hz. In each scanning process, only 8 elements would be excited in the ultrasonic phased array. Forty defects are modeled and studied, twenty of them are cubic and the others are sphere. The cylindrical phased array is used to transmit and receive the echo signals. The ultrasonic echo pulses are reflected from the defects and carried the shape information of the defects. Then, the WPT decompose algorithm is used to four-levers decompose, reconstruct and extract the feature of these echo signals. The reconstructed signals are used to the deep neural network to the defect classification. The output value(1, 0) and(0, 1) represent cubic defect and sphere defects, respectively. The influence of the distance between the cylindrical phased array and the defect is studied in each simulation process. Meanwhile the influence of the size of the defects is also considered. It is shown that the defects identification by firsthand data costs about 1550 s while the feature extracted data by the WPT method costs about 60 s. It shows that the cubic and sphere defects can be identified correctly by the cylindrical phased array. Results The feasibility of the defect defection and identification by the cylindrical phased array transducer is investigated based on the FEM analyses. Two types of defects(cubic and sphere) are investigated. The influence of the position and the size of the defects are considered in numerical simulation. It is found that the accuracy of the defects identification is about 95%. Compared with the defects identification with the firsthand data, the WPT method greatly improves the accuracy of the defect identification and computational consumption. Discussion and Conclusions Ultrasonic cylindrical phased array can be used to the defect detection and identification. It is found that the defects identification with high accuracy could be obtained by the cylindrical phased array. The WPT and deep neural network can greatly reduce the training time and classifying process. This paper will be of valuable help for the inner surface defection and defect identification.
Background, Motivation and Objective Ultrasonic phased array technology is widely used in industrial Non-Destructive Testing(NDT). However, the traditional planar phased array transducer is usually used to scan the object with flat surface. To imaging the inner surface of a borehole wall, an ultrasonic cylindrical phased array transducer is presented to fulfill the whole scanning process for the inner surface. On the other hand, conventional defects identification usually depends on the specific professional knowledge and the experience of the operating personnel which could cause the instability and individual differences of the analysis results easily. In this paper, a new method is presented and studied for defect detection and classification with the cylindrical phased array by the wavelet-packet transform(WPT) and deep neural network. Statement of Contribution/Methods The WPT is used to extract the signal feature of the ultrasonic echo from defects, and the deep neural network is used to classify the defects feature. Firstly, a finite element model is conducted to simulate the defects identification by the cylindrical phased array transducer. A series of simulation are done for two types of defects with different sizes by a 64-element cylindrical phased array transducer with the center frequency of 500 k Hz. In each scanning process, only 8 elements would be excited in the ultrasonic phased array. Forty defects are modeled and studied, twenty of them are cubic and the others are sphere. The cylindrical phased array is used to transmit and receive the echo signals. The ultrasonic echo pulses are reflected from the defects and carried the shape information of the defects. Then, the WPT decompose algorithm is used to four-levers decompose, reconstruct and extract the feature of these echo signals. The reconstructed signals are used to the deep neural network to the defect classification. The output value(1, 0) and(0, 1) represent cubic defect and sphere defects, respectively. The influence of the distance between the cylindrical phased array and the defect is studied in each simulation process. Meanwhile the influence of the size of the defects is also considered. It is shown that the defects identification by firsthand data costs about 1550 s while the feature extracted data by the WPT method costs about 60 s. It shows that the cubic and sphere defects can be identified correctly by the cylindrical phased array. Results The feasibility of the defect defection and identification by the cylindrical phased array transducer is investigated based on the FEM analyses. Two types of defects(cubic and sphere) are investigated. The influence of the position and the size of the defects are considered in numerical simulation. It is found that the accuracy of the defects identification is about 95%. Compared with the defects identification with the firsthand data, the WPT method greatly improves the accuracy of the defect identification and computational consumption. Discussion and Conclusions Ultrasonic cylindrical phased array can be used to the defect detection and identification. It is found that the defects identification with high accuracy could be obtained by the cylindrical phased array. The WPT and deep neural network can greatly reduce the training time and classifying process. This paper will be of valuable help for the inner surface defection and defect identification.
Background, Motivation and Objective Ultrasonic phased array technology is widely used in industrial Non-Destructive Testing(NDT). However, the traditional planar phased array transducer is usually used to scan the object with flat surface. To imaging the inner surface of a borehole wall, an ultrasonic cylindrical phased array transducer is presented to fulfill the whole scanning process for the inner surface. On the other hand, conventional defects identification usually depends on the specific professional knowledge and the experience of the operating personnel which could cause the instability and individual differences of the analysis results easily. In this paper, a new method is presented and studied for defect detection and classification with the cylindrical phased array by the wavelet-packet transform(WPT) and deep neural network. Statement of Contribution/Methods The WPT is used to extract the signal feature of the ultrasonic echo from defects, and the deep neural network is used to classify the defects feature. Firstly, a finite element model is conducted to simulate the defects identification by the cylindrical phased array transducer. A series of simulation are done for two types of defects with different sizes by a 64-element cylindrical phased array transducer with the center frequency of 500 k Hz. In each scanning process, only 8 elements would be excited in the ultrasonic phased array. Forty defects are modeled and studied, twenty of them are cubic and the others are sphere. The cylindrical phased array is used to transmit and receive the echo signals. The ultrasonic echo pulses are reflected from the defects and carried the shape information of the defects. Then, the WPT decompose algorithm is used to four-levers decompose, reconstruct and extract the feature of these echo signals. The reconstructed signals are used to the deep neural network to the defect classification. The output value(1, 0) and(0, 1) represent cubic defect and sphere defects, respectively. The influence of the distance between the cylindrical phased array and the defect is studied in each simulation process. Meanwhile the influence of the size of the defects is also considered. It is shown that the defects identification by firsthand data costs about 1550 s while the feature extracted data by the WPT method costs about 60 s. It shows that the cubic and sphere defects can be identified correctly by the cylindrical phased array. Results The feasibility of the defect defection and identification by the cylindrical phased array transducer is investigated based on the FEM analyses. Two types of defects(cubic and sphere) are investigated. The influence of the position and the size of the defects are considered in numerical simulation. It is found that the accuracy of the defects identification is about 95%. Compared with the defects identification with the firsthand data, the WPT method greatly improves the accuracy of the defect identification and computational consumption. Discussion and Conclusions Ultrasonic cylindrical phased array can be used to the defect detection and identification. It is found that the defects identification with high accuracy could be obtained by the cylindrical phased array. The WPT and deep neural network can greatly reduce the training time and classifying process. This paper will be of valuable help for the inner surface defection and defect identification.
引文