Looking for a vibrational measure of vehicle powertrain damage using multifractal analysis
Andrzej Puchalski1 , Iwona Komorska2
1, 2University of Technology and Humanities in Radom, Radom, Poland
Vibroengineering PROCEDIA, Vol. 3, 2014, p. 351-356.
Accepted 24 September 2014; published 10 October 2014
The paper proposes the Detrended Fluctuation Analysis (DFA) of the vibration signal for diagnosing of mechanical defects of the vehicle powertrain. The DFA allows investigations of the observed signals with regard to their multifractality. The results of vibration signal analysis of the engine with the damaged exhaust valve and with the unsuitable exhaust valve clearance are presented. During road test the acceleration vibration signal was recorded with additional signals for synchronization and engine timing. The vibration data are analysed by DFA and the resultant scaling-law curve with crossover points are obtained. The estimated Hurst exponents are used in the selection procedure of diagnostic features.
Keywords: combustion engine vibrations, defected valve system, detrended fluctuation analysis, Hurst exponent.
- Butar F. B., Kale M. Fractal analysis of time series and distribution properties of Hurst exponent. Journal of Mathematical Sciences and Mathematics Education, Vol. 6, No. 1, 2011, p. 8-19. [CrossRef]
- Hurst H. E. Long term storage capacity of reservoirs. Transactions of the American Society of Agricultural Engineers, Vol. 116, 1951, p. 770-799. [CrossRef]
- Peng C. K., Havlin S., Stanley H. E., Goldberger A. L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos, Vol. 5, 1995, p. 82-87. [CrossRef]
- Kantelhardt J. W., Zschiegner S. A., Koscielny-Bunde E., Havlin S., Bunde A., Stanley H. E. Multofractal detrended fluctuation analysis of nonstationary time series. Physica A, Vol. 316, 2002, p. 87-114. [CrossRef]
- Moura E. P., Vieira A. P., Irmao M. A. S., Silva A. A. Applications of detrended-fluctuation analysis to gearbox fault diagnosis. Mechanical Systems and Signal Processing, Vol. 23, 2009, p. 682-689. [CrossRef]
- Moura E. P., Souto C. R., Silva A. A., Irmao M. A. S. Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses. Mechanical Systems and Signal Processing, Vol. 25, 2011, p. 1765-1772. [CrossRef]
- Lin J., Chen Q. Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion. Mechanical Systems and Signal Processing, Vol. 38, 2013, p. 515-533. [CrossRef]
- Lin J., Chen Q. A novel method for feature extraction using crossover characteristics of nonlinear data and its application to fault diagnosis of rotary machinery. Mechanical Systems and Signal Processing, Vol. 48, 2014, p. 174-187. [CrossRef]
- Puchalski A., Komorska I. Application of vibration signal Kalman filtering to fault diagnostics of engine exhaust valve. Journal of Vibroegineering, Vol. 15, Issue 1, 2013, p. 152-158. [CrossRef]