A High-Sensitivity MFL Method for Tiny Cracks in Bearing Rings

Wu, Jianbo; Yang, Yun; Li, Erlong; Deng, Zhiyang; Kang, Yihua; Tang, Chaoqing; Sunny, Ali Imam

doi:10.1109/tmag.2018.2810199

Cited by 20 publications

(12 citation statements)

References 18 publications

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“…A magnetic induction head-based very sensitive MFL technique was developed [18,19]. To measure the change in magnetic flux detected in a magnetic core with an open gap, an induction coil was connected.…”

Section: A Electromagnetic Approachesmentioning

confidence: 99%

IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis

et al. 2022

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Rail is one of the most energy efficient and economical modes of transportation amongst many. Regular inspection of railway track health is essential for ensuring robust and secure train operations. Investigations and delayed discovery pose a serious risk to the safe functioning of rail transportation. The traditional method of manually examining the rail track using a railway cart is both inefficient and susceptible to mistakes and bias. It is imperative to automate inspection in order to avert catastrophes and save countless lives particularly in zones where train accidents are numerous. The purpose of this research is to develop an Internet of Things (IoT)-based autonomous railway track fault detection system to enhance the existing railway cart system in order to address the aforementioned issues. In addition to data collection on Pakistani railway lines, this work contributes significantly to railway track fault identification and classification based on acoustic analysis, as well as fault localization. Due to their frequency of occurrences, six types of track's faults were first targeted: wheel burnt, loose nuts and bolts, crash sleeper, creep, low joint, and point and crossing. Support vector machines, logistic regression, random forest, extra tree classifier, decision tree classifier, multilayer perceptron and ensemble with hard and soft voting were among the machine learning methods used. The results indicate that acoustic data can successfully assist in discriminating the track defects and localizing the defects in real time. The results show that MLP achieved the best results, with an accuracy of 98.4 percent.

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Section: A Electromagnetic Approachesmentioning

confidence: 99%

IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis

et al. 2022

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“…To find small defects in the rail surface, a ferrite is added to an LMF sensor to reduce the reluctance to increase the magnetic intensity above the defects [17]. Wu Jianbo proposes a high-sensitivity MFL method based on a magnetic induction head [18]. A magnetic core with an open gap is applied to guide leaked magnetic flux into a detection magnetic circuit, where an induction coil is placed to detect the change of the magnetic flux.…”

Section: Related Workmentioning

confidence: 99%

A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage

et al. 2021

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With the rapid development of the world’s railways, rail is vital to ensure the safety of rail transit. This article focuses on the magnetic flux leakage (MFL) non-destructive detection technology of the surface defects in railhead. A Multi-sensors method is proposed. The main sensor and four auxiliary sensors are arranged in the detection direction. Firstly, the root mean square (RMS) of the x-component of the main sensor signal is calculated. In the data more significant than the threshold, the defects are determined by the relative values of the sensors signal. The optimal distances among these sensors are calculated to the size of a defect and the lift-off. From the finite element simulation and physical experiments, it is shown that this method can effectively suppress vibration interference and improve the detection accuracy of defects.

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“…Flux leakage allows the inspector to localize and identify surface and subsurface flaws [3]. The inevitable advantages of this technique are high efficiency and no requirement for direct contact with the tested object [4,5]. However, it also has some disadvantages, such as susceptibility to the flaw orientation, the need to demagnetize the object after inspection, a sensitivity that is dependent on the distance between sensor and material, and difficulty detecting small and stress-induced changes [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Recording Method (MRM) for Nondestructive Evaluation of Ferromagnetic Materials

et al. 2022

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This paper proposes and experimentally investigates a novel nondestructive testing method for ferromagnetic elements monitoring, the Magnetic Recording Method (MRM). In this method, the inspected element must be magnetized in a strictly defined manner before operation. This can be achieved using an array of permanent magnets arranged to produce a quasi-sinusoidal magnetization path. The magnetic field caused by the original residual magnetization of the element is measured and stored for future reference. After the operation or loading, the magnetic field measurement is repeated. Analysis of relative changes in the magnetic field (for selected components) allows identifying applied stress. The proposed research methodology aims to provide information on the steel structure condition unambiguously and accurately. An interpretation of the results without referring to the original magnetization is also possible but could be less accurate. The method can be used as a standard technique for NDT (Non-Destructive Testing) or in structural health monitoring (SHM) systems.

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A High-Sensitivity MFL Method for Tiny Cracks in Bearing Rings

Cited by 20 publications

References 18 publications

IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis

IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis

A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage

Magnetic Recording Method (MRM) for Nondestructive Evaluation of Ferromagnetic Materials

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