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A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion

基于振动-电参量信息融合的大型轴流风机运行趋势预测方法

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Abstract

As the critical equipment, large axial-flow fan (LAF) is used widely in highway tunnels for ventilating. Note that any malfunction of LAF can cause severe consequences for traffic. Specifically, fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault. Thus, the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance (or noise). In order to overcome this problem, a novel early fault judgment method to predict the operation trend is proposed in this paper. The vibration-electric information fusion, the support vector machine (SVM) with particle swarm optimization (PSO), and the cross-validation (CV) for predicting LAF operation states are proposed and discussed. Finally, the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.

摘要

大型轴流式通风机作为公路隧道通风的关键设备, 得到了广泛的应用。轴流风机产生的任何故障都可能对交通造成严重后果。如果在早期故障阶段能检测到异常状态, 就可以极大地抑制故障劣化。针对大型轴流风机故障初期表征不明显, 以及由风机外壳对振动信号的滤波作用, 导致的所测信号对内部故障不敏感, 在线智能故障诊断及时性差、灵敏度低等问题, 提出一种基于振动信号-功率信息融合的大型轴流风机趋势预测方法。将提取的特征向量作为粒子群和交叉验证优化的支持向量机的输入实现轴流风机运行趋势的预测。实验结果表明, 该方法的性能优于基准方法。

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References

  1. REN L, XU Z Y, YAN X Q. Single-sensor incipient fault detection [J]. IEEE Sensors Journal, 2011, 11(9): 2102–2107. DOI: https://doi.org/10.1109/JSEN.2010.2093879.

    Article  Google Scholar 

  2. LV Y, FANG Fang, YANG Ting-ting, ROMERO C E. An early fault detection method for induced draft fans based on MSET with informative memory matrix selection [J]. ISA Transactions, 2020, 102: 325–334. DOI: https://doi.org/10.1016/j.isatra.2020.02.018.

    Article  Google Scholar 

  3. XU Xiao-gang, LIU Hai-xiao, ZHU Hao, WANG Song-ling. Fan fault diagnosis based on symmetrized dot pattern analysis and image matching [J]. Journal of Sound and Vibration, 2016, 374: 297–311. DOI: https://doi.org/10.1016/j.jsv.2016.03.030.

    Article  Google Scholar 

  4. SONG Yong-xing, WU Ke-lin, CHU Ning, WU Zhuan-wu. Research on fault diagnosis method of metro fan based on modulation intensity [J]. Chinese Journal of Turbomachinery, 2019, 61(1): 77–81. DOI: https://doi.org/10.16492/j.fjjs.2019.01.0011. (in Chinese)

    Google Scholar 

  5. ZHANG Wei, PENG Gao-liang, LI Chuan-hao, CHEN Yuanhang, ZHANG Zhu-jun. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals [J]. Sensors (Basel, Switzerland), 2017, 17(2): E425. DOI: https://doi.org/10.3390/s17020425.

    Article  Google Scholar 

  6. ZHANG Zhong-yun, WU Jian-de, MA Jun, WANG Xiaodong. Slight fault diagnosis for rolling bearing based on chaos and fractal theory [J]. Journal of Central South University (Science and Technology), 2016, 47(2): 640–646. (in Chinese)

    Google Scholar 

  7. WEN Cheng-lin, LV Fei-ya, BAO Zhe-jing, LIU Mei-qin. A review of data driven-based incipient fault diagnosis [J]. Acta Automatica Sinica, 2016, 42(9): 1285–1299. DOI: https://doi.org/10.16383/j.aas.2016.c160105. (in Chinese)

    MATH  Google Scholar 

  8. TANG Jian, QIAO Jun-fei, WU Zhi-wei, CHAI Tian-you, ZHANG Jian, YU Wen. Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features [J]. Mechanical Systems and Signal Processing, 2018, 99: 142–168. DOI: https://doi.org/10.1016/j.ymssp.2017.06.008.

    Article  Google Scholar 

  9. DURO J A, PADGET J A, BOWEN C R, KIM H A, NASSEHI A. Multi-sensor data fusion framework for CNC machining monitoring [J]. Mechanical Systems and Signal Processing, 2016, 66–67: 505–520. DOI: https://doi.org/10.1016/j.ymssp.2015.04.019.

    Article  Google Scholar 

  10. LU Chuan-qi, WANG Shao-ping, WANG Xing-jian. A multi-source information fusion fault diagnosis for aviation hydraulic pump based on the new evidence similarity distance [J]. Aerospace Science and Technology, 2017, 71: 392–401. DOI: https://doi.org/10.1016/j.ast.2017.09.040.

    Article  Google Scholar 

  11. WAN S T, PENG B. Early fault diagnosis method of rolling bearing based on nonlocal mean denoising and fast spectral correlation [J]. Journal of Central South University (Science and Technology), 2020, 51(1): 76–85. DOI: https://doi.org/10.11817/J.ISSN.1672-7207.2020.01.010. (in Chinese)

    Google Scholar 

  12. SONG Bing, TAN Shuai, SHI Hong-bo, ZHAO Bo. Fault detection and diagnosis via standardized k nearest neighbor for multimode process [J]. Journal of the Taiwan Institute of Chemical Engineers, 2020, 106: 1–8. DOI: https://doi.org/10.1016/j.jtice.2019.09.017.

    Article  Google Scholar 

  13. HU Juan, PENG Hong, WANG Jun, YU Wen-ping. kNN-P: A kNN classifier optimized by P systems [J]. Theoretical Computer Science, 2020, 817: 55–65. DOI: https://doi.org/10.1016/j.tcs.2020.01.001.

    Article  MathSciNet  Google Scholar 

  14. XU Fan, TSE P W. A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis [J]. Journal of Central South University, 2019, 26(9): 2404–2417. DOI: https://doi.org/10.1007/s11771-019-4183-7.

    Article  Google Scholar 

  15. DENG Wu, YAO Rui, ZHAO Hui-min, YANG Xin-hua, LI Guang-yu. A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm [J]. Soft Computing, 2019, 23(7): 2445–2462. DOI: https://doi.org/10.1007/s00500-017-2940-9.

    Article  Google Scholar 

  16. LI Zhong-mei, GUI Wei-hua, ZHU Jian-yong. Fault detection in flotation processes based on deep learning and support vector machine [J]. Journal of Central South University, 2019, 26(9): 2504–2515. DOI: https://doi.org/10.1007/s11771-019-4190-8.

    Article  Google Scholar 

  17. LIU B, NING Q, LIU C X, AI Q, HE P. Residual life prediction of rolling bearings based on continuous hidden Markov model and PSO-SVM [J]. Journal of Computer Applications, 2019, 39(S1): 31–35. (in Chinese)

    Google Scholar 

  18. HUANG Hong-zhong, HUANG Cheng-geng, PENG Zhaochun, LI Yan-feng, YIN Heng-su. Fatigue life prediction of fan blade using nominal stress method and cumulative fatigue damage theory [J]. International Journal of Turbo & Jet-Engines, 2020, 37(2): 135–139. DOI: https://doi.org/10.1515/tjj-2017-0015.

    Article  Google Scholar 

  19. FOUCHÉ L B, UREN K R, SCHOOR G V. Energy-based visualisation of an axial-flow compressor system for the purposes of fault detection and diagnosis [J]. IFAC-PapersOnLine, 2016, 49(7): 314–319. DOI: https://doi.org/10.1016/j.ifacol.2016.07.311.

    Article  Google Scholar 

  20. DURO J A, PADGET J A, BOWEN C R, KIM H A, NASSEHI A. Multi-sensor data fusion framework for CNC machining monitoring [J]. Mechanical Systems and Signal Processing, 2016, 66–67: 505–520. DOI: https://doi.org/10.1016/j.ymssp.2015.04.019.

    Article  Google Scholar 

  21. LU Chuan-qi, WANG Shao-ping, WANG Xing-jian. A multi-source information fusion fault diagnosis for aviation hydraulic pump based on the new evidence similarity distance [J]. Aerospace Science and Technology, 2017, 71: 392–401. DOI: https://doi.org/10.1016/j.ast.2017.09.040.

    Article  Google Scholar 

  22. PEETERS C, GUILLAUME P, HELSEN J. Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy [J]. Renewable Energy, 2018, 116: 74–87. DOI: https://doi.org/10.1016/j.renene.2017.01.056.

    Article  Google Scholar 

  23. LONG X F, YANG P, GUO H X, WU X W. Review of Fault diagnosis methods for large wind turbines [J]. Power System Technology, 2017, 41(11): 3480–3490. (in Chinese)

    Google Scholar 

  24. KUO B C, HO H H, LI C H, HUNG C C, TAUR J S. A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 317–326. DOI: https://doi.org/10.1109/JSTARS.2013.2262926.

    Article  Google Scholar 

  25. JIANG Gao-xia, WANG Wen-jian. Error estimation based on variance analysis of k-fold cross-validation [J]. Pattern Recognition, 2017, 69: 94–106. DOI: https://doi.org/10.1016/j.patcog.2017.03.025.

    Article  Google Scholar 

  26. TAO Peng, LIU Jian, LIANG Tian-xi. Research on fault diagnosis method of axial flow induced draft fan of power plant based on machine learning [C]// 2019 4th International Conference on System Reliability and Safety (ICSRS). Rome, Italy: IEEE, 2019: 325–330. DOI: https://doi.org/10.1109/ICSRS48664.2019.8987662.

    Google Scholar 

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Corresponding author

Correspondence to Zhen-yu Gu  (谷振宇).

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Foundation item

Project(2018YFB2002100) supported by the National Key R&D Program of China

Contributors

GU Zhen-yu provided the concept and edited the draft of manuscript. ZHU Yao-yao conducted the literature review and wrote the first draft of the manuscript. XIANG Ji-lei edited the draft of manuscript. And ZENG Yuan analyzed the calculated results.

Conflict of interest

GU Zhen-yu, ZHU Yao-yao, XIANG Ji-lei and ZENG Yuan declare that they have no conflict of interest.

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Gu, Zy., Zhu, Yy., Xiang, Jl. et al. A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion. J. Cent. South Univ. 28, 1786–1796 (2021). https://doi.org/10.1007/s11771-021-4629-6

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