Non-stationary signal analysis based on general parameterized time--frequency transform and its application in the feature extraction of a rotary machine

Abstract

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time–frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

Key words： rotary machines; condition monitoring; fault diagnosis; GPTFT; SCI

Acknowledgements

The authors gratefully acknowledge the support provided by the National Natural Science Foundation of China (Grant Nos. 11632011, 11472170, 51421092, and 11572189) to this work.

References

1 Chu F, Peng Z, Feng Z, . Modern Signal Processing Approach in Machine Fault Diagnosis. Beijing: Science Press, 2009 (in Chinese)

2 Yang P. Data mining diagnosis system based on rough set theory for boilers in thermal power plants. Frontiers of Mechanical Engineering, 2006, 1(2): 162–167

3 Li W, Shi T, Yang S. An approach for mechanical fault classification based on generalized discriminant analysis. Frontiers of Mechanical Engineering, 2006, 1(3): 292–298

4 Chen X, Wu W, Wang H, . Distributed monitoring and diagnosis system for hydraulic system of construction machinery. Frontiers of Mechanical Engineering, 2010, 5(1): 106–110

5 Wang S, Chen T, Sun J. Design and realization of a remote monitoring and diagnosis and prediction system for large rotating machinery. Frontiers of Mechanical Engineering, 2010, 5(2): 165–170

6 Su H, Shi T, Chen F, . New method of fault diagnosis of rotating machinery based on distance of information entropy. Frontiers of Mechanical Engineering, 2011, 6(2): 249–253

7 Yang Y. Theory, methodology of parameterized time-frequency analysis and its application in engineering signal processing. Dissertation for the Doctoral Degree. Shanghai: Shanghai Jiao Tong University, 2013 (in Chinese)

8 Yang Y, Peng Z, Dong X, . General parameterized time-frequency transform. IEEE Transactions on Signal Processing, 2014, 62(11): 2751–2764

9 Mihovilovic D, Bracewell R. Adaptive chirplet representation of signals on time-frequency plane. Electronics Letters, 1991, 27(13): 1159–1161

10 Mann S, Haykin S. ‘Chirplets’ and ‘warblets’: Novel time-frequency methods. Electronics Letters, 1992, 28(2): 114–116

11 Angrisani L, D’Arco M, Schiano Lo Moriello R, . On the use of the warblet transform for instantaneous frequency estimation. IEEE Transactions on Instrumentation and Measurement, 2005, 54(4): 1374–1380

12 Yang Y, Peng Z, Meng G, . Characterize highly oscillating frequency modulation using generalized Warblet transform. Mechanical Systems and Signal Processing, 2012, 26: 128–140

13 Gribonval R. Fast matching pursuit with a multiscale dictionary of Gauss chirps. IEEE Transactions on Signal Processing, 2001, 49(5): 994–1001

14 Angrisani L, D’Arco M. A measurement method based on modificed version of the chirplet transform for instantaneous frequenct estimation. IEEE Transactions on Instrumentation and Measurement, 2002, 51(4): 704–711

15 Candès E J, Charlton P R, Helgason H. Detecting highly oscillatory signals by chiplet path pursuit. Applied and Computational Harmonic Analysis, 2008, 24(1): 14–40

16 Zou H, Dai Q, Wang R, . Parametric TFR via windowed exponential frequency modulated atoms. IEEE Signal Processing Letters, 2001, 8(5): 140–142

17 Yang Y, Peng Z, Dong X, . Application of parameterized time-frequency analysis on multicomponent frequency modulated signals. IEEE Transactions on Instrumentation and Measurement, 2014, 63(12): 3169–3180

18 Huang N, Shen Z, Long S, . The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A, 1998, 454(1971): 903–995

19 Wu Z, Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 2009, 1(1): 1–41

20 Mallat S G, Zhang Z. Matching pursuit in a time-frequency dictionary. IEEE Transactions on Signal Processing, 1993, 41(12): 3397–3415

21 Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Review, 2001, 43(1): 129–159

22 Dragomiretskiy K, Zosso D. Variational mode composition. IEEE Transactions on Signal Processing, 2014, 62(3): 531–544

23 Chen S, Yang Y, Wei K, . Time-varying frequency-modulated component extraction based on parameterized demodulation and singular value decomposition. IEEE Transactions on Instrumentation and Measurement, 2016, 65(2): 276–285

24 Peng Z, Meng G, Chu F, . Polynomial chirplet transform with application to instantaneous frequency estimation. IEEE Transactions on Instrumentation and Measurement, 2011, 60(9): 3222–3229

25 Yang Y, Peng Z, Meng G, . Spline-kernelled chirplet transform for the analysis of signals with time-varying frequency and its application. IEEE Transactions on Industrial Electronics, 2012, 59(3): 1612–1621