## 2 Experimental Design
### Data Preprocessing  
In the experiment, because the detection system could test wire rope of different diameters, we selected 28 mm and 30 mm diameter wire rope as testing specimens; their structure was 6 \(\times\) 37 (the rope consisted of 6 strands and each strand had 37 steel wires). Artificial defect types included one discontinuity to five and seven broken wires; moreover, each defect was destroyed as small gap, curling wires, and wide fracture. Besides, we tested the system performance of abrasive wire, and part defects of a specimen rope are shown in Figure 5. The length of this rope was 6 m, and its permitting detection was 4.5 m because of the magnetic pole. There was an abrasive defect at 1.1 m; 1.46 m and 1.67 m separately had a one-wire broken defect. There were two-wire broken defects at 1.22 and 2.02 m, three-wire broken defects at 2.52 m, five-wire broken defects at 3.04 and 3.54 m, seven-wire broken defects at 3.97 m, and four-wire broken defects at 4.31 m. The width of all small gaps was not more than 3 mm, the height of curling wires was about 0.5 cm, and wide fracture was about 1 cm. Implementing the system to detect the specimen rope above, we acquired surface UME array signals, spreading the raw data by channel number, as shown in Figure 6. In the red circles, these singular signals were MFL signals of one or two broken wires in the gap condition. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Testing Step** & **Value (dm2)** & **Weight (kg)** & **Detection Time (s)** \\ \hline Remanence & Detection after magnetizing & 11.5 & 2.5 & 30+ 1,2 \\ UME & Direct detection & 6.5 3 & 1 3 & 24 1 \\ \hline \hline \end{tabular}  
* Detection speed of remanence was the same as UME, and slow testing speed can guarantee compete sampling;
* Magnetizing time of remanence was 6 s every time, and it mostly needed to be implemented more than three times in the experiment; 3 Core components were transplanted to a remanence assembly.  
\end{table} 
Table 1: Comparison of the remanence device with UME. Figure 5: Manufacturing standard small gap defects: (**a**) worn wire at 1.1 m; (**b**) two wires broken at 1.22 m; (**c**) one wire broken at 1.46 m; (**d**) one wire broken at 1.67 m; (**e**) two wires broken at 2.02 m; (**f**) three wires broken at 2.52 m; (**g**) five wires broken at 3.04 m; (**h**) five wires broken 3.54 m; (**i**) seven wires broken at 3.97 m; (**j**) four wires broken at 4.31. It can be seen in Figure 6 that both ends had magnetic pole for magnetization. There the magnetic field strength exceeded sensor sensitivity range, thus there was no fluctuant curve. Besides, because of local magnetizing, sensor differences, and channel imbalance, the baseline of testing signal was not coherent. Thus, the data need to be preprocessed, including removing outliers and estimating baseline. The magnetic field distribution is continuous in the air, therefore adjacent sampling points would not differ severely. We adopted Equation (1) to process the outliers, where the outlier was replaced by the mean of two adjacent points [23]:  
\[X^{\prime}_{i,j}=(X_{i,j+1}+X_{i,j-1})/2|_{|X_{i,j-}X_{i,j+1}|>T\cap|X_{i,j-}X_ {i,j-1}|>T} \tag{1}\]  
where \(i\) is the channel number, \(j\) is the sampling length, and \(T\) is a threshold and was set as 50 in this paper. After removing the outliers, a promoted subsection mean method was implemented to estimate the baseline of each signal, then removed to acquire the balanced MFL array signal. This method was based on the mean of MFL signal in a strand period of zero. The estimation algorithm was as shown in Equation (2):  
\[s(i)=x(i)-\frac{1}{2m}\sum_{j=i-m+1}^{m}x(i+j)(i=m,m+1,m+2\cdot\cdot\cdot N) \tag{2}\]  
where \(s(i)\) is the MFL signal without baseline, \(x(i)\) is the input signal, \(m\) is the sampling points of lay length, and \(N\) is the length of signal.