# Rail Magnetic Flux Leakage Detection and Data Analysis Based on Double-Track Flaw Detection Vehicle

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## Abstract

**:**

## 1. Introduction

## 2. MFL Technique

#### 2.1. Magnetic Dipole Model

#### 2.2. Magnetic Flux Leakage Sensor

## 3. Quantitative Simulation of Defect Depth

_{Bx}= 0.1451ln(b) + 0.2705 and V

_{Bz}= 0.0869ln(b) + 0.1212 were obtained using simulation data, which could be used to predict defect depth. The following section provides a test validation on the demarcated defect via the double-track rail flaw detection vehicle.

## 4. Design of MFL System and Conditioning Circuit

#### 4.1. Sliding Shoe Magnetic Flux Leakage Probe

#### 4.2. Signal Conditioning Circuit

_{G}, with the magnification of 1–10,000. The formula for magnification was as follows:

_{G}with the digital potentiometer AD8403. The adjustable resistance of AD8403 ranged from 50 Ω to 1 kΩ, and the magnification was 50 to 200 times through formula derivation, which met the sampling range of acquisition card. The resistance was adjusted by controlling the microcontroller in the hardware circuit through an acquisition program.

## 5. Test and Analysis of Artificial Defect Samples

#### 5.1. Introduction of the Defect Samples

#### 5.2. Analysis of the Detection Results of Defects at Different Depths

#### 5.3. Analysis of the Features of Defects at Different Depths

_{Bx}= 0.6126ln(b) + 0.0141 in the Bx direction and V

_{Bz}= 2.7787ln(b) + 0.0087 in the Bz direction. The peak-to-peak value in the Bx direction was small at 2.7 and 4 mm depth, and large at 5 mm depth. The Bz direction basically conformed to the fitted logarithmic function. The function model fitted with the experimental results of the Bx and Bz directions basically tallied with the data in the simulation.

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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Defect | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|

Depth | 5 | 4 | 3.5 | 3 | 2.7 | 2 | 1.5 | 1 |

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**MDPI and ACS Style**

Wang, Y.; Wang, Y.; Wang, P.; Ji, K.; Wang, J.; Yang, J.; Shu, Y. Rail Magnetic Flux Leakage Detection and Data Analysis Based on Double-Track Flaw Detection Vehicle. *Processes* **2023**, *11*, 1024.
https://doi.org/10.3390/pr11041024

**AMA Style**

Wang Y, Wang Y, Wang P, Ji K, Wang J, Yang J, Shu Y. Rail Magnetic Flux Leakage Detection and Data Analysis Based on Double-Track Flaw Detection Vehicle. *Processes*. 2023; 11(4):1024.
https://doi.org/10.3390/pr11041024

**Chicago/Turabian Style**

Wang, Yi, Yuhui Wang, Ping Wang, Kailun Ji, Jun Wang, Jie Yang, and Yuan Shu. 2023. "Rail Magnetic Flux Leakage Detection and Data Analysis Based on Double-Track Flaw Detection Vehicle" *Processes* 11, no. 4: 1024.
https://doi.org/10.3390/pr11041024