ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Original Paper

LMD-BPF method for modal parameter identification and its applications

Funds:  Supported by Aeronautical Science(2014ZD08007).
Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.03.003
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  • Corresponding author: BIAN Jie(Corresponding author), male, born in 1985,Master/Advanced Engineer.Research field: Vibration and fault diagnosis of aeroengine and helicopter transmission system. E-mail: bianjie_hrbeu@163.com
  • Received Date: 24 December 2018
  • Accepted Date: 31 May 2019
  • Rev Recd Date: 31 May 2019
  • Publish Date: 31 March 2020
  • In view of the difficulties existing in the accurate identification of structures’ modal parameters, a modal parameter identification method based on local mean decomposition (LMD) and band-pass filtering (BPF) was proposed. Firstly, the LMD method was used in the decomposition of a displacement simulation signal and a frequency measuring signal of a compressor guide vane, and modal aliasing phenomenon existed in the decomposed PF components; Then, the LMD-BPF method was utilized in the decomposition of the displacement simulation signal and frequency testing signal of the guide vane, and each modal frequency was accurately and successfully separated; Finally, the LMD-BPF modal parameter identification method was employed to identify the modal parameters of the displacement simulation signal and frequency testing signal of the guide vane. The maximum errors between the four modal frequencies and damping ratios of the identified displacement simulation signal and the corresponding theoretical values were 0.205% and 2.387%, respectively. The maximum difference between the three modal frequencies of the identified guide vane and the testing frequencies was less than 1.0%, and the maximum difference between the three modal damping ratios of the identified guide vane and those identified by half power bandwidth method was less than 0.65%. The simulation analysis and experimental study of modal parameter identification verified the validity of the proposed method.
    In view of the difficulties existing in the accurate identification of structures’ modal parameters, a modal parameter identification method based on local mean decomposition (LMD) and band-pass filtering (BPF) was proposed. Firstly, the LMD method was used in the decomposition of a displacement simulation signal and a frequency measuring signal of a compressor guide vane, and modal aliasing phenomenon existed in the decomposed PF components; Then, the LMD-BPF method was utilized in the decomposition of the displacement simulation signal and frequency testing signal of the guide vane, and each modal frequency was accurately and successfully separated; Finally, the LMD-BPF modal parameter identification method was employed to identify the modal parameters of the displacement simulation signal and frequency testing signal of the guide vane. The maximum errors between the four modal frequencies and damping ratios of the identified displacement simulation signal and the corresponding theoretical values were 0.205% and 2.387%, respectively. The maximum difference between the three modal frequencies of the identified guide vane and the testing frequencies was less than 1.0%, and the maximum difference between the three modal damping ratios of the identified guide vane and those identified by half power bandwidth method was less than 0.65%. The simulation analysis and experimental study of modal parameter identification verified the validity of the proposed method.
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    [2]
    CHEN K F, ZHANG S W. Improvement on the damping estimation by half power point method[J]. Journal of Vibration Engineering, 2002, 15: 151-155.
    [3]
    LI Y G, YAO Z, LIU J, et al. A novel STFT of window duration increasing optimization based on instantaneous frequency[J]. Journal of Northeastern University: Natural Science, 2007, 28: 1737-1740.
    [4]
    HE R, LUO W B, WANG B L. A new method of choosing scales in wavelet transform for damping identification[J]. Journal of Harbin Institute of Technology (New Series), 2008, 15: 164-166.
    [5]
    LEI Y G, LIN J, HE Z J, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2013, 35: 108-126.
    [6]
    CHENG J S, YANG Y, YANG Y. A rotating machinery fault diagnosis method based on local mean decomposition[J]. Digital Signal Processing, 2012, 22: 356-366.
    [7]
    HU J S, YANG S X, WU Z T, et al. The comparison of vibration signals’ time-frequency analysis between EMD-based HT and STFT method in rotating machinery[J]. Turbine Technology, 2002, 44: 336-338.
    [8]
    QIAO B D, CHEN G, QU X X. A rolling bearing coupling fault diagnosis method based wavelet transform and blind source separation[J]. Mechanical Science and Technology for Aerospace Engineering, 2012, 31: 53-58.
    [9]
    DOU D Y, ZHAO Y K. Application of ensemble empirical mode decomposition in failure[J]. Transactions of the CSAE, 2010, 26: 190-196.
    [10]
    LI N, CAO Y R, CHENG L. The mode mixing of empirical mode decomposition in mechanical fault diagnosis[J]. Journal of Air Force Engineering University: Natural Science Edition, 2014, 15: 76-80.
    [11]
    ZHANG X L, JIAO W D. Fault diagnosis analysis and research based on LMD EMD[J]. Mechanical Research and Application, 2012, 21: 156-158.
    [12]
    LI Q, SONG W Q. Bearing vibration signal reconstruction based on LMD and nonconvex penalized Lq minimization compressed sensing[J].Journal of Central South University: Science and Technology, 2015, 46: 3696-3702.
    [13]
    HAN J P, LU G F, CAO W S. Research of the transient disturbance detection technology of power system using local mean decomposition algorithm[J]. Journal of Zhengzhou University: Engineering Science, 2016, 37: 29-33, 59.
    [14]
    CHENG J S, ZHENG J D, YANG Y. A nonstationary signal analysis approach-the local characteristic-scale decomposition method[J]. Journal of Vibration Engineering, 2012, 25: 215-220.
    [15]
    SUN W, XIONG B S, HUANG J P, et al. Fault diagnosis of a rolling bearing using wavelet packet de-noising and LMD[J].Journal of Vibration and Shock, 2012, 31: 153-156.
    [16]
    YAO Q B, YANG Z C, LI B. Damping identification of materials using wavelet transform[J]. Mechanical Science and Technology for Aerospace Engineering, 2007, 26: 850-855.
    [17]
    WANG C, ZHU H P, WANG B. Identifying damping ratio of cable structures using complex wavelet transform[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2012, 40: 115-118.
    [18]
    YING H Q, LIU J M, SHEN S. Half-power bandwidth method and INV damping ration solver study[J]. Noise and Vibration Control, 2006, 25: 4-6.
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Catalog

    [1]
    WANG S C, DENG Z Q, GAO H B, et al. Experimental investigation on mechanical property of metal rubber used in lunar lander in high or low temperature[J]. Journal of Aeronautical Materials, 2004, 24: 27-31.
    [2]
    CHEN K F, ZHANG S W. Improvement on the damping estimation by half power point method[J]. Journal of Vibration Engineering, 2002, 15: 151-155.
    [3]
    LI Y G, YAO Z, LIU J, et al. A novel STFT of window duration increasing optimization based on instantaneous frequency[J]. Journal of Northeastern University: Natural Science, 2007, 28: 1737-1740.
    [4]
    HE R, LUO W B, WANG B L. A new method of choosing scales in wavelet transform for damping identification[J]. Journal of Harbin Institute of Technology (New Series), 2008, 15: 164-166.
    [5]
    LEI Y G, LIN J, HE Z J, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2013, 35: 108-126.
    [6]
    CHENG J S, YANG Y, YANG Y. A rotating machinery fault diagnosis method based on local mean decomposition[J]. Digital Signal Processing, 2012, 22: 356-366.
    [7]
    HU J S, YANG S X, WU Z T, et al. The comparison of vibration signals’ time-frequency analysis between EMD-based HT and STFT method in rotating machinery[J]. Turbine Technology, 2002, 44: 336-338.
    [8]
    QIAO B D, CHEN G, QU X X. A rolling bearing coupling fault diagnosis method based wavelet transform and blind source separation[J]. Mechanical Science and Technology for Aerospace Engineering, 2012, 31: 53-58.
    [9]
    DOU D Y, ZHAO Y K. Application of ensemble empirical mode decomposition in failure[J]. Transactions of the CSAE, 2010, 26: 190-196.
    [10]
    LI N, CAO Y R, CHENG L. The mode mixing of empirical mode decomposition in mechanical fault diagnosis[J]. Journal of Air Force Engineering University: Natural Science Edition, 2014, 15: 76-80.
    [11]
    ZHANG X L, JIAO W D. Fault diagnosis analysis and research based on LMD EMD[J]. Mechanical Research and Application, 2012, 21: 156-158.
    [12]
    LI Q, SONG W Q. Bearing vibration signal reconstruction based on LMD and nonconvex penalized Lq minimization compressed sensing[J].Journal of Central South University: Science and Technology, 2015, 46: 3696-3702.
    [13]
    HAN J P, LU G F, CAO W S. Research of the transient disturbance detection technology of power system using local mean decomposition algorithm[J]. Journal of Zhengzhou University: Engineering Science, 2016, 37: 29-33, 59.
    [14]
    CHENG J S, ZHENG J D, YANG Y. A nonstationary signal analysis approach-the local characteristic-scale decomposition method[J]. Journal of Vibration Engineering, 2012, 25: 215-220.
    [15]
    SUN W, XIONG B S, HUANG J P, et al. Fault diagnosis of a rolling bearing using wavelet packet de-noising and LMD[J].Journal of Vibration and Shock, 2012, 31: 153-156.
    [16]
    YAO Q B, YANG Z C, LI B. Damping identification of materials using wavelet transform[J]. Mechanical Science and Technology for Aerospace Engineering, 2007, 26: 850-855.
    [17]
    WANG C, ZHU H P, WANG B. Identifying damping ratio of cable structures using complex wavelet transform[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2012, 40: 115-118.
    [18]
    YING H Q, LIU J M, SHEN S. Half-power bandwidth method and INV damping ration solver study[J]. Noise and Vibration Control, 2006, 25: 4-6.

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