Vibration-based feature extraction of determining dynamic characteristic for engine block low vibration design

Xian-feng Du , Zhi-jun Li , Feng-rong Bi , Jun-hong Zhang , Xia Wang , Kang Shao

Journal of Central South University ›› 2012, Vol. 19 ›› Issue (8) : 2238 -2246.

PDF
Journal of Central South University ›› 2012, Vol. 19 ›› Issue (8) : 2238 -2246. DOI: 10.1007/s11771-012-1268-y
Article

Vibration-based feature extraction of determining dynamic characteristic for engine block low vibration design

Author information +
History +
PDF

Abstract

In order to maintain vibration performances within the limits of the design, a vibration-based feature extraction method for dynamic characteristic using empirical mode decomposition (EMD) and wavelet analysis was proposed. The proposed method was verified experimentally and numerically by implementing the scheme on engine block. In the implementation process, the following steps were identified to be important: 1) EMD technique in order to solve the feature extraction of vibration signals; 2) Vibration measurement for the purpose of confirming the structural weak regions of engine block in experiment; 3) Finite element modeling for the purpose of determining dynamic characteristic in time region and frequency region to affirm the comparability of response character corresponding to improvement schemes; 4) Adopting a feature index of IMF for structural improvement based on EMD and wavelet analysis. The obtained results show that IMF of signal is more sensitive to response character corresponding to improvement schemes. Finally, examination of the results confirms that the proposed vibration-based feature extraction method is very robust, and focuses on the relative merits of modification and full-scale structural optimization of engine, together with the creation of new low-vibration designs.

Keywords

feature extraction / dynamic characteristic / finite element model / empirical mode decomposition / diesel engine block

Cite this article

Download citation ▾
Xian-feng Du, Zhi-jun Li, Feng-rong Bi, Jun-hong Zhang, Xia Wang, Kang Shao. Vibration-based feature extraction of determining dynamic characteristic for engine block low vibration design. Journal of Central South University, 2012, 19(8): 2238-2246 DOI:10.1007/s11771-012-1268-y

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

LeeN., ParkW., RuotoloR., TrombleyD.. NVH development of EU5 2.0L and 2.2L diesel engine [C]. SAE 2011 World Congress and Exhibition, 2011Detroit, MI, United StatesSAE

[2]

WangY., JiangY. C.. New time-frequency distribution based on the polynomial Wigner-Ville distribution and L class of Wigner-Ville distribution [J]. IET Signal Processing, 2010, 4(2): 130-136

[3]

YanZ. H., MiyamotoA., JiangZ. W.. Frequency slice wavelet transform for transient vibration response analysis [J]. Mechanical Systems and Signal Processing, 2009, 23(5): 1474-1489

[4]

SheenY. T., HungC. K.. Constructing a wavelet-based envelope function for vibration signal analysis [J]. Mechanical Systems and Signal Processing, 2004, 18(1): 119-126

[5]

SubasiA.. Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction [J]. Computers in Biology and Medicine, 2007, 37(2): 227-244

[6]

LoutridisS., DoukaE., TrochidisA.. Crack identification in double-cracked beams using wavelet analysis [J]. Journal of Sound and Vibration, 2004, 277(4/5): 1025-1039

[7]

HuangN. E., ShenZ., LongS. R., WuM. L. C., ShihH. H., ZhengQ. N., YenN. C., TungC. C., LiuH. H.. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society of London Series, 1998, 454(1971): 903-995

[8]

HuangN. E.. Review of empirical mode decomposition [J]. Wavelet Applications, 2001, 4391: 71-80

[9]

ChengJ. S., YuD. J., YangY.. Research on the intrinsic mode function (IMF) criterion in EMD method [J]. Mechanical Systems and Signal Processing, 2006, 20(4): 817-822

[10]

LoutridisS. J.. Instantaneous energy density as a feature for gear fault detection [J]. Mechanical Systems and Signal Processing, 2006, 20(5): 1239-1253

[11]

WuF.-j., QuL.-sheng.. Diagnosis of subharmonic faults of large rotating machinery based on EMD [J]. Mechanical Systems and Signal Processing, 2009, 23(2): 467-475

[12]

DuQ.-h., YangS.-nian.. Application of the EMD method in the vibration analysis of ball bearings [J]. Mechanical Systems and Signal Processing, 2007, 21(6): 2634-2644

[13]

PareyA., Ei BadaouiM., GuilletF., TandonN.. Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect [J]. Journal of Sound and Vibration, 2006, 294(3): 547-561

[14]

ZouY., TongL., StevenG. P.. Vibration-based model-dependent damage identification and health monitoring for composite structures-A review [J]. Journal of Sound and Vibration, 2000, 230(2): 357-378

[15]

DiebalaA., OuelaaN., HamzaouiN.. Detection of rolling bearing defects using discrete wavelet analysis [J]. Meccanica, 2008, 43(3): 339-348

[16]

HuangN. E., WuM. L., QuW. D., LongS. R., ShenS. S. P.. Applications of Hilbert-Huang transform to non-stationary financial time series analysis [J]. Applied Stochastic Models in Business and Industry, 2003, 19(3): 245-268

[17]

LiH.-l., DengX.-y., DaiH.-liang.. Structural damage detection using the combination method of EMD and wavelet analysis [J]. Mechanical Systems and Signal Processing, 2007, 21(1): 298-306

[18]

DuX.-f., LiZ.-j., BiF.-r., ZhangJ.-h., WangX., ShaoKang.. Structural topography optimization of engine block to minimize vibration based on sensitivity analysis [J]. Advanced Materials Research, 2011, 291/292/293/294: 318-326.

AI Summary AI Mindmap
PDF

95

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/