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Abnormal Signals Elimination in Hardness Evaluation Using Barkhausen Noise and Tangential Magnetic Field

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Abstract

In this paper, magnetic Barkhausen noise (MBN) and tangential magnetic field (TMF) are employed to quantitatively predict the hardness of bearing steel GCr15. In order to solve the problems that MBN and TMF signals are susceptible to electromagnetic interference (EMI) and sensor vibration during the inspection process, which lead to the decrease of the hardness prediction accuracy, a feature-based abnormal signal elimination algorithm is proposed. The features of MBN and TMF signals are used to determine whether the signals are affected by EMI or sensor vibration. To verify the effectiveness of the algorithm, the multiple linear regression (MLR) and multilayer perceptron (MLP) hardness prediction model are developed based on MBN and TMF features. After removing abnormal signals, the hardness prediction error of MLR model is reduced from 21.39 to 1.25% and the hardness prediction error of MLP model is reduced from 7.75 to 0.13%.

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Data Availability

The datasets are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by National Key Research and Development Program of China (Grant No. 2018YFB2003304), in part by National Natural Science Foundation of China (Grant No. 61871218), in part by National Key Research and Development Program of China (Grant Nos. 2018YFB2100903, 2017YFF0107304, 2017YFF0209700), and in part by Fundamental Research Funds for the Central Universities (Grant Nos. NJ2019007, NJ2020014).

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, China

    Cheng Hang, Wenbo Liu, Wangcai Chen, Lei Li, Ping Wang, Yi Lin, Haixia Zhu & Kaiyu Li

  2. Nondestructive Detection and Monitoring Technology for High Speed Transportation Facilities, Key Laboratory of Ministry of Industry and Information Technology, Nanjing, 211106, Jiangsu, China

    Cheng Hang, Wangcai Chen, Lei Li & Ping Wang

  3. Chair of Non-Destructive Testing and Quality Assurance, Saar-University, 66125, Saarbrücken, Saarland, Germany

    Gerd Dobmann

Authors
  1. Cheng Hang
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  2. Wenbo Liu
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  3. Gerd Dobmann
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  4. Wangcai Chen
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  5. Lei Li
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  7. Yi Lin
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  8. Haixia Zhu
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  9. Kaiyu Li
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Contributions

CH: conceptualization, methodology, software, writing—original draft. WL: supervision, writing—review & editing. GD: methodology, writing—review & editing. WC: writing—review & editing. LL: methodology, validation. PW: funding acquisition, supervision. YL: validation. HZ: supervision. KL: supervision.

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Correspondence to Cheng Hang.

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Hang, C., Liu, W., Dobmann, G. et al. Abnormal Signals Elimination in Hardness Evaluation Using Barkhausen Noise and Tangential Magnetic Field. J Nondestruct Eval 42, 15 (2023). https://doi.org/10.1007/s10921-023-00924-2

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