Analysis of MFL Model for Sucker Rod Defects and Its MFL Signal Processing

Zhang, Ou; Wei, Xueye

doi:10.1520/jte20170687

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…The MFL signal collected by each Hall sensor is, where dim(X)=n and p denotes the channel number. It is one of 1, 2 to n. The MFL signals are denoised by means of amplifying circuit and band-pass filtering to obtain ̃ [19]. The denoising performed by preprocessing is purposed to filter out the detail components (the high frequency noise and direct component) from MFL signal while preserving the contour information in the signal (the low frequency defect).…”

Section: Stage 1：preprocessingmentioning

confidence: 99%

Adaptive Filtering Method of MFL Signal on Rail Top Surface Defect Detection

Wang

Jia

et al. 2021

IEEE Access

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Magnetic flux leakage (MFL) detection technology provides an effective method to conduct high-speed detection of the damage suffered by rail surface. With regard to high-speed detection, there is frequently a complex noise contained in the magnetic signal of railway leakage, which is similar to the amplitude of defect and the overlaps of frequency spectrum. In this paper, an improved adaptive filtering method is proposed to solve the problem caused by filtering the MFL signal on the rail top surface. Through the characteristics of distribution shown by defects on the top surface of the railway and those of the data collected by the rail top array sensor, this method is applied to construct a virtual channel containing almost only interference signals but no defects. Then, in combination with the adaptive filtering algorithm, the virtual channel signal is taken as the reference input of the adaptive canceller, each single channel MFL signal is taken as the original input of the adaptive canceller, and the filtered MFL signal is taken as the output. Then, the MFL signal of rail top is collected by the train at the speed of 30km / h on the manual calibration line. According to the experimental results, the noise intensity of MFL signal is reduced by up to 81.44%. In addition, the filtering method is adopted to process MFL signals with different directions and varying detection speed. As indicated by the results, the noise intensity of MFL signal is reduced by more than 74%. INDEX TERMS High speed rail detection; Magnetic flux leakage signal; Adaptive filtering algorithm; Reference signal; Adaptive noise canceller. KAILUN JI received the B.Eng. and the M.S. degree in techniques and instruments of measurement from the Nanjing University of Aeronautics and Astronautics, China, in 2016 and 2020. He is currently pursuing the Ph.D. degree at the Nanjing University of Aeronautics and Astronautics of China. His research interests include nondestructive testing, signal processing and system modeling.

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Section: Stage 1：preprocessingmentioning

confidence: 99%

Adaptive Filtering Method of MFL Signal on Rail Top Surface Defect Detection

Wang

Jia

et al. 2021

IEEE Access

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“…At present, the leakage magnetic detection method for steel wire ropes presents the advantages of high efficiency and reliability, and the overhead ground wire and steel wire ropes have similar structural characteristics and material properties, so the leakage magnetic detection method has a huge potential in the field of defect detection of overhead ground wire [ 17 , 18 ]. Refs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Defect Detection Method for Overhead Ground Wire

Xiao,

Xiong,

Zhang

et al. 2023

Sensors

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Overhead ground wires typically have strong axial tension and are prone to structural defects caused by corrosion and lightning strikes, which could lead to serious safety hazards. Therefore, it is important to detect defects accurately and quickly to avoid those problems. Existing defect detection methods for overhead ground wires are mainly traditional metal defect detection methods, including eddy current detection, ultrasonic detection, and manual visual inspection. However, those methods have problems of low detection efficiency, high environmental requirements, and insufficient reliability. To solve the above problems, this paper studies a novel type of defect detection technology for overhead ground wire. Firstly, the magnetic leakage characteristics around the defects of overhead ground wires are analyzed, and the defect detection device is designed. Then, the influence of air gap, lift-off distance, defect width, and cross-sectional loss rate on the magnetic flux leakage signal is studied, a novel defect detection method for overhead ground wire is proposed, and experimental verification is carried out. The results show that the proposed method can accurately locate and quantify the defect, which has the advantages of good reliability and high efficiency and lays the foundation for preventing accidents caused by defective overhead ground wires.

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A data‐driven approach for flow corrosion characteristic parameters prediction in an air cooler

Jin

Xiang-Yao

Liu

et al. 2020

Asia-Pacific J Chem Eng

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A data‐driven soft measurement method based on a multiunit back propagation neural network (MBPNN) is presented in this study. This model aims to estimate the characteristic parameters that can reflect the flow corrosion of the reactor effluent air cooler (REAC). Flow corrosion failure during the hydrogenation process presents a serious concern to the petrochemical industry. In this paper, a safety evaluation of flow corrosion failure for a petrochemical diesel hydrogenation unit is first carried out. During the investigation, it is found that there is a risk of NH4Cl crystallization at around 187°C. Then, considering flow‐induced corrosion and ammonium salt deposition, main characteristic parameters are determined, including NH4Cl crystallization temperature (TC), air cooler tube bundle minimum and maximum flow rate (Vmin and Vmax), air cooler inlet liquid water content (CW), and NH4HS concentration (CA). Finally, the data‐driven model based on a multiunit back propagation neural network (MBPNN) is constructed. An improved particle swarm optimization (PSO) approach is employed to initialize the main parameters of the model. Compared with a multioutput back propagation neural network (BPNN) model and MBPNN model without an optimization algorithm, the presented data‐driven model is proved to have high accuracy, a fast convergence rate, and high reliability.

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Analysis of MFL Model for Sucker Rod Defects and Its MFL Signal Processing

Cited by 10 publications

References 30 publications

Adaptive Filtering Method of MFL Signal on Rail Top Surface Defect Detection

Adaptive Filtering Method of MFL Signal on Rail Top Surface Defect Detection

A Novel Defect Detection Method for Overhead Ground Wire

A data‐driven approach for flow corrosion characteristic parameters prediction in an air cooler

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