Skip to main content
SpringerLink
Log in

Weak-fault diagnosis using state-transition-algorithm-based adaptive stochastic-resonance method

基于状态转移自适应随机共振的微弱故障诊断

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-based adaptive stochastic resonance (SR) method is proposed, which provides an alternative solution to the problem that the traditional SR has fixed parameters or optimizes only a single parameter and ignores the interaction between parameters. To be specific, the frequency-shifted and re-scaling are firstly used to pre-process an actual large signal to meet the requirement of the adiabatic approximate small parameter. And then, the signal-to-noise ratio is used as the optimization target, and the STA-based adaptive SR is used to synchronously optimize the system parameters. Finally, the optimal extraction and frequency recovery of a weak characteristic signal from a broken rotor bar fault are realized. The proposed method is compared with the existing methods by the early broken rotor bar experiments of traction motor. Experiment results show that the proposed method is better than the other methods in extracting weak signals, and the validity of this method is verified.

摘要

针对高速列车系统早期故障发生时特征信号微弱且淹没在强背景噪声之中的问题, 提出了一种 状态转移自适应随机共振方法。该方法解决了传统随机共振固定参数或只对单一参数进行优化、忽略 参数之间交互作用的不足。首先, 利用移频变尺度对实际大信号进行预处理, 使信号满足绝热近似小 参数的要求; 然后, 以信噪比作为优化目标, 采用状态转移随机共振对系统参数进行同步优化; 最终, 实现转子断条故障微弱特征信号的最优提取和频率恢复。通过对牵引电机早期转子断条实验进行比 较, 结果表明, 所提方法的微弱信号提取效果明显优于其他算法, 验证了该方法的有效性。

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. CHEN Bing-quan, CUI Jin-ge, XU Qing, SHU Ting, LIU Hong-li. Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image [J]. Journal of Central South University, 2019, 26(1): 120–131. DOI: https://doi.org/10.1007/s11771-019-3987-9.

    Article  Google Scholar 

  2. QIU Jian-bin, GAO Hui-jun, DING S X. Recent advances on fuzzy-model-based nonlinear networked control systems: A survey [J]. IEEE Transactions on Industrial Electronics, 2016, 63(2): 1207–1217. DOI: https://doi.org/10.1109/tie.2015.2504351.

    Article  Google Scholar 

  3. YU Jian-bo, LV Jing-xiang. Weak fault feature extraction of rolling bearings using local mean decomposition-based multilayer hybrid denoising [J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(12): 3148–3159. DOI: https://doi.org/10.1109/tim.2017.2751878.

    Article  Google Scholar 

  4. MULIMANI M, KOOLAGUDI S G. Segmentation and characterization of acoustic event spectrograms using singular value decomposition [J]. Expert Systems with Applications, 2019, 120: 413–425. DOI: https://doi.org/10.1016/j.eswa.2018.12.004.

    Article  Google Scholar 

  5. ZHAO Xue-zhi, YE Bang-yan. Singular value decomposition packet and its application to extraction of weak fault feature [J]. Mechanical Systems and Signal Processing, 2016, 70: 73–86. DOI: https://doi.org/10.1016/j.ymssp.2015.08.033.

    Article  Google Scholar 

  6. YOGANAND S, MOHAN B M. Denoising of ECG signals using multiwavelet transform [J]. HELIX, 2018, 8(1): 2696–2700. DOI: https://doi.org/10.29042/2018-2696-2700.

    Article  Google Scholar 

  7. CHEN Zhi-wen, DING S X, PENG Tao, YANG Chun-hua, GUI Wei-hua. Fault detection for non-gaussian processes using generalized canonical correlation analysis and random algorithms [J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1559–1567. DOI: https://doi.org/10.1109/TIE.2017.2733501.

    Article  Google Scholar 

  8. CHEN Zhi-wen, YANG Chun-hua, PENG Tao, DAN Hang-bing, LI Chang-geng, GUI Wei-hua. A cumulative canonical correlation analysis-based sensor precision degradation detection method [J]. IEEE Transactions on Industrial Electronics, 2019, 66(8): 6321–3330. DOI: https://doi.org/10.1109/TIE.2018.2873100.

    Article  Google Scholar 

  9. YIN Shen, DING S X, ZHOU Dong-hua. Diagnosis and prognosis for complicated industrial systems—part I [J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 2501–2505. DOI: https://doi.org/10.1109/TIE.2016.2522944.

    Article  Google Scholar 

  10. SU Xiao-jie, XIA Feng-qin, WU Li-gang. Event-triggered fault detector and controller coordinated design of fuzzy systems [J]. IEEE Transactions on Fuzzy Systems, 2018, 26(4): 2004–2016. DOI: https://doi.org/10.1109/TFUZZ.2017.2757459.

    Article  Google Scholar 

  11. GAMMAITONI L, HÄNGGI P, JUNG P, MARCHESONI F. Stochastic resonance [J]. Reviews of Modern Physics, 1998, 70(1): 223–287. DOI: https://doi.org/10.1103/revmodphys.70.223.

    Article  Google Scholar 

  12. QIAO Zi-jian, LEI Ya-guo, LI Nai-peng. Applications of stochastic resonance to machinery fault detection: A review and tutorial [J]. Mechanical Systems and Signal Processing, 2019, 122: 502–536. DOI: https://doi.org/10.1016/j.ymssp.2018.12.032.

    Article  Google Scholar 

  13. LIU Wei-xin, WANG Yu-jia, LIU Xing, ZHANG Ming-jun. Weak thruster fault detection for AUV based on stochastic resonance and wavelet reconstruction [J]. Journal of Central South University, 2016, 23(11): 2883–2895. DOI: https://doi.org/10.1007/s11771-016-3352-1.

    Article  Google Scholar 

  14. LI Ji-meng, LI Ming, ZHANG Jin-feng, JIANG Guo-jiang. Frequency-shift multiscale noise tuning stochastic resonance method for fault diagnosis of generator bearing in wind turbine [J]. Measurement, 2019, 133(2019): 421–432. DOI: https://doi.org/10.1016/j.measurement.2018.10.054.

    Article  Google Scholar 

  15. ZHANG Gang, SHI Jia-bei, ZHANG Tian-qi. Stochastic resonance in an under-damped linear system with nonlinear frequency fluctuation [J]. Physica A, 2018, 512: 230–240. DOI: https://doi.org/10.1016/j.physa.2018.08.016.

    Article  MathSciNet  Google Scholar 

  16. LU Si-liang, HE Qing-bo, WANG Jun. A review of stochastic resonance in rotating machine fault detection [J]. Mechanical Systems and Signal Processing, 2019, 116(2019): 230–260. DOI: https://doi.org/10.1016/j.ymssp.2018.06.032.

    Article  Google Scholar 

  17. QIAO Zi-jian, LEI Ya-guo, LIN Jin, JIA Feng. An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis [J]. Mechanical Systems and Signal Processing, 2017, 84(1): 731–746. DOI: https://doi.org/10.1016/j.ymssp.2016.08.030.

    Article  Google Scholar 

  18. DOLCE M, CARDONE D. Mechanical behavior of shape memory alloys for seismic applications: Austenite NiTi wires subjected to tension [J]. International Journal of Mechanical Sciences, 2001, 43(11): 2657–2677. DOI: https://doi.org/10.1016/S0020-7403(01)00050-9.

    Article  Google Scholar 

  19. XIA Jun-zhong, LIU Yuan-hong, MA Zong-bo, LENG Yong-gang, AN Xiang-bi. Weak signal detection based on the modulated stochastic resonance [J]. Journal of Vibration and Shock, 2012, 31(3): 132–135.140. (in Chinese)

    Google Scholar 

  20. LI Ji-meng, CHEN Xue-feng, HE Zheng-jia. Adaptive stochastic resonance method for impact signal detection based on sliding window [J]. Mechanical Systems and Signal Processing, 2013, 36(2): 240–255. DOI: https://doi.org/10.1016/j.ymssp.2012.12.004.

    Article  Google Scholar 

  21. QIN Yi, TAO Yi, HE Ye, TANG Bao-ping. Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction [J]. Journal of Sound and Vibration, 2014, 333(26): 7386–7400. DOI: https://doi.org/10.1016/j.jsv.2014.08.039.

    Article  Google Scholar 

  22. HE Chang-bo, LI Hong-kun, LI Zhi-xiong, ZHAO Xin-wei. An improved bistable stochastic resonance and its application on weak fault characteristic identification of centrifugal compressor blades [J]. Journal of Sound and Vibration, 2019, 442: 677–697. DOI: https://doi.org/10.1016/j.jsv.2018.11.016.

    Article  Google Scholar 

  23. ZHAO Jian, YANG Jian-hua, ZHANG Jing-ling, WU Cheng-jin, HUANG Da-wen. Improving the stochastic resonance in a bistable system with the bounded noise excitation [J]. Journal of Statistical Physics, 2018, 173(6): 1688–1697. DOI: https://doi.org/10.1007/s10955-018-2145-3.

    Article  MathSciNet  MATH  Google Scholar 

  24. ZHOU Xiao-jun, YANG Chun-hua, GUI Wei-hua. State transition algorithm [J]. Journal of Industrial of Industrial and Management Optimization, 2012, 8(4): 1039–1056. DOI: https://doi.org/10.1023/B:JOGO.0000015313.93974.b0.

    Article  MathSciNet  MATH  Google Scholar 

  25. WANG Guo-wei, YANG Chun-hua, ZHU Hong-qiu, LI Yong-gang, PENG Xiong-wei, GUI Wei-hua. State-transition-algorithm-based resolution for overlapping linear sweep voltammetric peaks with high signal ratio [J]. Chemometrics and Intelligent Laboratory Systems, 2016, 151(2): 61–70. DOI: https://doi.org/10.1016/j.chemolab.2015.12.008.

    Article  Google Scholar 

  26. ROCIO A LM, CARLOS R D, EDUARDO C Y. Novel FPGA-based methodology for early broken rotor bar detection and classification through homogeneity estimation [J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1760–1769. DOI: https://doi.org/10.1109/tim.2017.2664520.

    Article  Google Scholar 

  27. WANG Chun-sheng, SHA Chun-yang, SU Mei, HU Yu-kun. An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter [J]. Journal of Central South University, 2017, 24(2): 478–488. DOI: https://doi.org/10.1007/s11771-017-3450-8.

    Article  Google Scholar 

  28. YANG Chun-hua, YANG Chao, PENG Tao, YANG Xiao-yue, GUI Wei-hua. Fault-injection strategy for traction drive control systems [J]. IEEE Transactions on Industrial Electronics, 2017, 64(7): 5719–5727. DOI: https://doi.org/10.1109/TIE.2017.2674610.

    Article  Google Scholar 

  29. YANG Xiao-yue, YANG Chun-hua, PENG-Tao, CHEN Zhi-wen, LIU Bo, GUI Wei-hua. Hardware-in-the-loop fault injection for traction control system [J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2018, 6(2): 696–706. DOI: https://doi.org/10.1109/jestpe.2018.2794339.

    Article  Google Scholar 

  30. YIN Jin-tian, XIE Yong-fang, PENG Tao, YANG Chun-hua, CHEN Zhi-wen. Current characteristics analysis and fault injection of an early weak fault in broken rotor bar of traction motor [J]. Mathematical Problems in Engineering, DOI: https://doi.org/10.1155/2018/4934720.

  31. YIN Jin-tian, XIE Yong-fang, YANG Chun-hua. Monitoring of incipient rotor bars broken fault in traction motors based on RVMD method [J]. Control and Decision, 2018, 33(3): 497–502. (in Chinese)

    Google Scholar 

  32. GEORGOULAS G, CLIMENTE V, ANTONINO-DAVIU J A, TSOUMAS I, STYLIOS C, ANTERO A, GEORGE N. The use of a multilabel classification framework for the detection of broken bars and mixed eccentricity faults based on the start-up transient [J]. IEEE Transactions on Industrial Informatics, 2017, 13(2): 625–634. DOI: https://doi.org/10.1109/TII.2016.2637169.

    Article  Google Scholar 

  33. MOUSSA M A, BOUCHERMA M, KHEZZAR A. A detection method for induction motor bar fault using sidelobes leakage phenomenon of the sliding discrete fourier transform [J]. IEEE Transactions on Power Electronics, 2017, 32(7): 5560–5572. DOI: https://doi.org/10.1109/TPEL.2016.2605821.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Jin-tian Yin  (尹进田), Yong-fang Xie  (谢永芳), Zhi-wen Chen  (陈志文), Tao Peng  (彭涛) & Chun-hua Yang  (阳春华)

  2. Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang, 422000, China

    Jin-tian Yin  (尹进田)

Authors
  1. Jin-tian Yin  (尹进田)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong-fang Xie  (谢永芳)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhi-wen Chen  (陈志文)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Peng  (彭涛)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chun-hua Yang  (阳春华)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi-wen Chen  (陈志文).

Additional information

Foundation item

Projects(61490702, 61773407, 61803390, 61751312) supported by the National Natural Science Foundation of China; Project(61725306) supported by the National Science Foundation for Distinguished Young Scholars of China; Project(61621062) supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China; Project(2017TP1002) supported by Hunan Provincial Key Laboratory, China; Project(6141A0202210) supported by the Program of the Joint Pre-research Foundation of the Chinese Ministry of Education; Project(61400030501) supported by the General Program of the Equipment Pre-research Field Foundation of China; Project(2016TP1023) supported by the Science and Technology Project in Hunan Province Hunan Science and Technology Agency of China; Project(2018FJ34) supported by the Science and Technology Project in Shaoyang Science and Technology Agency of China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, Jt., Xie, Yf., Chen, Zw. et al. Weak-fault diagnosis using state-transition-algorithm-based adaptive stochastic-resonance method. J. Cent. South Univ. 26, 1910–1920 (2019). https://doi.org/10.1007/s11771-019-4123-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-019-4123-6

Key words

关键词

Search

Navigation