## 1 Introduction  
Magnetic flux leakage (MFL) technology is widely used in nondestructive testing (NDT) methods and is extensively researched for application in evaluating wire rope safety for reliable ferromagnetic materials. Traditional MFL equipment saturates the inner magnetic field in wire rope using permanent magnets or coils; because the permeability of the defect region is sharply reduced, magnetic lines of flux go through the air and then back to the rope. Thus, there is leakage of the magnetic field around the defect region [1], where the magnetic distribution is relevant to the width and depth of the defect [2]. Finite element analysis has been applied to research the relationship between defect features and MFL distribution, particularly in pipeline testing [2; 3; 4]. However, in wire rope detection, there were major defects of broken wires, abrasion, corrosion, and diameter shrinkage; among these, broken wire is the most dangerous defect for security [5]. To achieve qualitative detection in traditional MFL technology, a coil testing head was employed to obtain magnetic flow on the wire rope surface [6; 7], then the system judged the defect through a threshold. Because one-dimensional magnetic flow signal only could reflect axial defect information but no circumferential properties, integrated sensors were used to acquire MFL array signals [8; 9; 10; 11]. Hall sensors ordinarily were uniformly distributed on the circumference of saturated magnetic wire rope, and a controller was used to acquire the MFL array data [5], because image description is a good visual in two directions, reflecting more information about defects. Digital image processing was used to dispose defect images and exact characteristic vectors after converting array data to gray image [12]. The purpose of testing wire rope was to quantify defects. Support vector machine was applied to depict defect parameters [2], back propagation (BP) neural network achieved quantitative multidiscontinuity recognition [13] or classification of percentage of broken wires [7; 9], and because of good classification performance, radial basis function (RBF) neural network was applied to quantitative recognition of broken wires [10]. Owing to strong magnetic equipment, the wire rope was magnetized to saturation by a coil circuit, system power consumption was large, and installation was tedious at the site [14]. Moreover, this system needed a well-considered yoke to gather the magnetic field, forming a mutual flux loop even using permanent magnets [5; 15; 16]; this part sharply increased the weight of the device. Meanwhile, detection results were easily affected by the excitation source in the strong magnetic method, and the magnetic shielding device was designed to isolate the main magnetic flux and MFL. To overcome these disadvantages of the traditional MFL method, [9; 10] proposed a novel testing technology using GMR sensors, which was based on remanence of ferromagnetic materials. This detection device separated the testing head and excitation source and removed the yokes to reduce the system weight. However, its reliability was poor for testing small gaps where the signal-to-noise ratio (SNR) was low. The other wire rope detection methods included ultrasonic guided wave [17], ray method [18], magnetostrictive guidedwave [19], eddy current method [13], and optics. However, these methods have been researched less and have obvious limitations in usage, such as inconvenient installation, poor anti-interference, high maintenance cost, radioactive contamination, and easily affecting surface conditions. Signal processing is an important step in NDT testing, and promoting SNR is the basic goal of processing. Furthermore, the algorithm should be easily implemented online and have good computation performance. Wavelet is the most common tool in MFL signal application, such as wavelet multiresolution analysis [20], wavelet threshold [21], and wavelet denoising based on compressed sensing [9].