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Exploring real-time fault detection of high-speed train traction motor based on machine learning and wavelet analysis

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Abstract

The implementation of real-time fault detection technology for key components of high-speed train traction electromechanical system is of great significance for improving train motor reliability and reducing guarantee costs. It has become an inevitable trend. Machine learning is a new and powerful means of researching fault detection technology. Based on machine learning, this paper conducts real-time fault detection technology research on the electromechanical actuators of the key components of electromechanical systems. A model of the electromechanical actuator is established, and the mechanism, influence and fault injection method of the three faults of the electromechanical actuator motor shaft jamming, gear broken tooth, excessive ball screw clearance and two faults of internal leakage are analyzed. On this basis, a fault simulation model of the traction motor of a high-speed train was built to obtain simulation fault data. At the same time, based on wavelet packet decomposition and reconstruction, the fault simulation data of electromechanical actuators and hydraulic pumps are analyzed, the wavelet packet energy distribution is calculated, and time-domain statistics are combined to extract energy feature vectors that can reflect component fault characteristics. This paper proposes a fault diagnosis method for electromechanical actuators based on machine learning, designs, and improves the neural network learning algorithm and network parameters, and improves the classification effect of the neural network; proposes a fault diagnosis method based on the GA-SVM algorithm, using real values. The coded genetic algorithm improves the parameter optimization of the support vector machine, and improves the classification speed of the support vector machine. Finally, the effectiveness and superiority of the two fault diagnosis methods designed in this paper are verified on their respective objects.

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References

  1. Ali MZ, Shabbir MNSK, Zaman SMK et al (2020) Single-and multi-fault diagnosis using machine learning for variable frequency drive-fed induction motors. IEEE Trans Ind Appl 56(3):2324–2337

    Article  Google Scholar 

  2. Nishat Toma R, Kim JM (2020) Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms. Appl Sci 10(15):5251

    Article  Google Scholar 

  3. Sadiq MT, Yu X, Yuan Z et al (2019) Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform. IEEE Access 7:127678–127692

    Article  Google Scholar 

  4. Kumar P, Hati AS (2020) Review on machine learning algorithm based fault detection in induction motors. Arch Comput Methods Eng 5:1–12

    Google Scholar 

  5. Lee HK, Choi YS (2018) A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequency image. IEEE Access 12:906–909

    Google Scholar 

  6. Behri M, Subasi A, Qaisar SM (2018) Comparison of machine learning methods for two class motor imagery tasks using EEG in brain-computer interface. IEEE Access 19:1–5

    Google Scholar 

  7. Krishnaveni S, SenthilrajaRaja S, Jayasankar T et al (2020) Analysis and control of the motor vibration using arduino and machine learning model. Mater Today Proc 3:56–72

    Google Scholar 

  8. Ali MZ, Shabbir MNSK, Liang X et al (2018) Experimental investigation of machine learning based fault diagnosis for induction motors. IEEE Access 7:1–14

    Google Scholar 

  9. Toma RN, Prosvirin AE, Kim JM (2020) Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors 20(7):1884

    Article  Google Scholar 

  10. Kurkin S, Chholak P, Maksimenko V et al (2019) Machine learning approaches for classification of imaginary movement type by meg data for neurorehabilitation. IEEE Trans Instrum Meas 5(1):106–108

    Google Scholar 

  11. Isa NEM, Amir A, Ilyas MZ et al (2019) Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique. Bull Electr Eng Inform 8(1):269–275

    Article  Google Scholar 

  12. Hsueh YM, Ittangihal VR, Wu WB et al (2019) Fault diagnosis system for induction motors by CNN using empirical wavelet transform. Symmetry 11(10):1212

    Article  Google Scholar 

  13. Sabir R, Rosato D, Hartmann S et al (2019) LSTM based bearing fault diagnosis of electrical machines using motor current signal. J Mech Sci Technol 613–618

  14. Xu B, Zhang L, Song A et al (2018) Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. IEEE Access 7:6084–6093

    Article  Google Scholar 

  15. Zaman SMK, Liang X (2021) An effective induction motor fault diagnosis approach using graph-based semi-supervised learning. IEEE Access 9:7471–7482

    Article  Google Scholar 

  16. Deng W, Zhang S, Zhao H et al (2018) A novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing. IEEE Access 6:35042–35056

    Article  Google Scholar 

  17. Vinayak BA, Anand KA, Jagadanand G (2019) Wavelet-based real-time stator fault detection of inverter-fed induction motor. IET Electr Power Appl 14(1):82–90

    Article  Google Scholar 

  18. Bravo-Imaz I, Ardakani HD, Liu Z et al (2017) Motor current signature analysis for gearbox condition monitoring under transient speeds using wavelet analysis and dual-level time synchronous averaging. Mech Syst Signal Process 94:73–84

    Article  Google Scholar 

  19. Lee HK, Choi YS (2019) Application of continuous wavelet transform and convolutional neural network in decoding motor imagery brain-computer interface. Entropy 21(12):1199

    Article  Google Scholar 

  20. Chaudhary S, Taran S, Bajaj V et al (2020) A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications. Comput Methods Programs Biomed 187:105325

    Article  Google Scholar 

Download references

Acknowledgements

The study was supported by “The Special Project of Shanxi Province Education Science “1331 Project” (Grant No.ZX-18050); The Science Foundation for Youths of Shanxi Datong University (Grant No.2018Q7)”.

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  1. College of Mechanical and Electrical Engineering, Shanxi Datong University, Datong, 037009, China

    Yanshu Li

  2. College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian, 116028, China

    Yanshu Li

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  1. Yanshu Li
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Correspondence to Yanshu Li.

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Li, Y. Exploring real-time fault detection of high-speed train traction motor based on machine learning and wavelet analysis. Neural Comput & Applic 34, 9301–9314 (2022). https://doi.org/10.1007/s00521-021-06284-0

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  • DOI: https://doi.org/10.1007/s00521-021-06284-0

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