# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
## 6 Conclusions  
We presented a method for efficient eddy current detection and recognition of the defects in the girth welds of pipelines. This method uses an embedded system based on image processing with a back propagation neural network. Conventional internal testing methods of pipelines, e.g. the magnetic flux leakage technique and the ultrasonic technique, are not able to detect defects of the girth weld [7; 8]. However, the eddy current testing method based on the impedance diagram analysis proposed in this paper could effectively identify the cracks in the girth weld. In contrast with the conventional eddy current testing [9; 10], we used the two-dimensional impedance diagram of the eddy current detection signal and selected the geometric characteristic parameters of the impedance diagram with proper classification effect using the intra-class scattering matrix. Then, an image processing algorithm was used to extract the characteristic parameters of the impedance diagram that were considered as the network input. Finally, the back propagation neural network was trained using the training set, which realized an automatic recognition of the defect signals. The identification accuracy of the system is high, which provides a new idea for non-destructive testing of the pipes and overcomes the shortcoming that conventional pipeline inspection can not detect the defects in the girth weld effectively. Moreover, we implemented the defect recognition algorithm using a FPGA and achieved higher computational performance. The real-time online analysis could be achieved under lower power consumption and provides eddy current detection and recognition of the girth weld defects in a pipeline inner surface using an embedded system. The implementation of this system provides a basis for the pipeline pig application. The limitations of the proposed system are as follows. Due to the limitation of the machining accuracy, the minimum depth of the cracks is about 2.5 mm and the width is about 0.7 mm. It could only judge the existence of the cracks in the girth weld, but cannot determine the characteristics of the defects, such as depth, width, length, morphology and other parameters. In response to this problem and to enrich the training set, the processing method of the experimental specimens needs to be improved, and more kinds of specimens need to be tested. The speed of the data acquisition and the recognition need to be further improved in order to be applied to the pipeline pigs. The detection range is limited by the number and size of the probe, and only small areas may be detected. To solve this problem, it is necessary to study the design and application of the array probes. For the crack in the girth weld, the conventional magnetic flux leakage technique and ultrasonic technique may not be applicable. That makes the eddy current testing a breakthrough. The design of the eddy current array probe and corresponding signal processing methods should be seriously taken into account, which could expand the detection area and may greatly facilitate the development of defect imaging technology. [PERSON] and [PERSON] proposed the idea of this paper. [PERSON] and [PERSON] developed the hardware system and designed the experiment. [PERSON] and [PERSON] helped carry out the experiment. [PERSON] and [PERSON] performed the classification algorithm. [PERSON] and [PERSON] wrote the manuscript. All authors reviewed the manuscript.