# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
## 1 Introduction  
With rapid economic and social development, pipeline transportation has become one of the most efficient and economical ways of transporting material. However, the safe operation of pipelines represents an increasingly serious challenge considering the large-scale development in the pipeline transportation industry and pipeline mileage enhancement. Regular inspection and safety assessments of pipeline defects are of great significance to the prediction and timely elimination of pipeline safety hazards [1,2].  
In general, pipelines are formed by metals and intended for long-term operation. They are vulnerable to the damp soil, internal electrolytes and erosion of transport materials, resulting in cracks, deformations and other defects which may eventually lead to undesired accidents [3]. The girth welds represent the weakest parts of the pipeline, which are limited by the construction quality level and insufficient control of the welding quality. Cracks are the main form of pipe failure, where the leakage in a sufficiently large crack may result in environmental damage and do harm to humans. According to the US Department of Transportation Pipeline and Hazardous Materials Safety Administration [4], in 2010-2012 there were eight pipeline accidents caused by girth welding cracks. According to theresearch of [PERSON] et al. [5], at present, oil and gas pipeline crack accidents are mainly caused by inner surface cracks of the girth welds. However, the surface morphology of the girth welds is complex and difficult to assess. As a result, the detection of defects in the inner surface of the pipe, especially those superimposed on the circumferential welds, represents the key challenging point of non-destructive pipeline testing.  
At present, the most common method for pipeline inspection is to use a pig equipped with non-destructive testing equipment [6]. The nondestructive testing equipment method is mostly based on magnetic flux leakage testing [7] (MFLT) and electromagnetic acoustic transducers (EMATs). Nevertheless, MFLT suffers from low precision and is incapable of detecting small defects, and the EMATs are susceptible to interference, which results in poor signal quality [8]. As a consequence, these two approaches cannot effectively identify girth weld defects. On the other hand, eddy current testing (ECT) technology is endowed with the advantages of high sensitivity, high precision and non-contact, and may be efficiently applied to automatic detection [9; 10; 11; 12; 13]. It has been widely used for the non-destructive testing of pipelines, especially for the detection of the girth weld defects in the inner surface [14; 15]. Eddy current testing is typically accomplished by qualitative and quantitative assessment of the defects through the comparison of a test signal to a reference signal. However, a high probability for misjudgment may be expected since the method often largely relies on the experience of the examiner.  
This paper introduces a method for the embedded recognition of girth weld defects in the inner surface of the pipe by leveraging image processing, neural network techniques, and ECT technology. In order to accurately and intelligently judge the defects, the system assumes the geometric characteristic parameters as the input and the presence or absence of the defects as the output, where the neural network algorithm could be very useful. Moreover, the neural network algorithm offers many advantages such as simple learning rules, non-linear mapping and easy computer realization. To recognize girth weld defects, we first synthesize the impedance diagram of the eddy current signal, and then use image processing to extract the geometric characteristics of the diagram with proper sorting features, selected using a class scattering matrix. Finally, a recognition algorithm is applied using a FPGA (Field-Programmable Gate Array). The method provided has high reliability, high recognition speed, low power consumption and high accuracy.