Abstract
The rotating machinery, as a typical example of large and complex mechanical systems, is prone to diversified sorts of mechanical faults, especially on their rotating components. Although they can be collected via vibration measurements, the critical fault signatures are always masked by overwhelming interfering contents, therefore difficult to be identified. Moreover, owing to the distinguished time-frequency characteristics of the machinery fault signatures, classical dyadic wavelet transforms (DWTs) are not perfect for detecting them in noisy environments. In order to address the deficiencies of DWTs, a pseudo wavelet system (PWS) is proposed based on the filter constructing strategies of wavelet tight frames. The presented PWS is implemented via a specially devised shift-invariant filterbank structure, which generates non-dyadic wavelet subbands as well as dyadic ones. The PWS offers a finer partition of the vibration signal into the frequency-scale plane. In addition, in order to correctly identify the essential transient signatures produced by the faulty mechanical components, a new signal impulsiveness measure, named spatial spectral ensemble kurtosis (SSEK), is put forward. SSEK is used for selecting the optimal analyzing parameters among the decomposed wavelet subbands so that the masked critical fault signatures can be explicitly recognized. The proposed method has been applied to engineering fault diagnosis cases, in which the processing results showed its effectiveness and superiority to some existing methods.
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References
Randall R B. Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications. New York: Wiley, 2011
Yan R Q, Gao R X. Energy-based feature extraction for defect diagnosis in rotary machines. IEEE T Instrum Meas, 2009, 58(9): 3130–3139
Peng Z K, Chu F L. Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mech Syst Signal Pr, 2004, 18(2): 199–221
Wang S B, Huang W G, Zhu Z K. Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis. Mech Syst Signal Pr, 2011, 25(4): 1299–1320
Yuan J, He Z J, Zi Y Y, et al. Gearbox fault diagnosis of rolling mills using multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution. Sci China Ser E-Tech Sci, 2009, 52(10): 2801–2809
Cheng W, Zhang Z S, He Z J. Enhance the separation performance of ICA via cludtering evaluation and its applications. Adv Sci Lett, 2011, 4(6–7): 1951–1956
Cheng W, Lee L S, Zhang Z S, et al. Independent component analysis based source number estimation and its application for mechanical systems. J Sound Vib, 2012, 23(5): 5153–5167
Cheng W, Zhang Z S, Lee S L, et al. Source contribution evaluation of mechanical vibration signals via enhanced independent component analysis. J Manuf Sci E-T ASME, 2012, 134(2): 0210104
Pan Y, Chen J, Li X L. Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means. Mech Syst Signal Pr, 2010, 24(2): 559–566
Gao L X, Yang Z J, Cai L G, et al. Roller bearing fault diagnosis based on nonlinear redundant lifting wavelet packet analysis. Sensors, 2011, 11(1): 260–277
He Z J, Zi Y Y, Chen X F, et al. Transform principle of inner product for fault diagnosis. J Vibr Eng, 2007, 20(5): 528–533
Peng Z K, Jackson M R, Rongong J A, et al. On the energy leakage of discrete wavelet transform. Mech Syst Signal Process, 2009, 23(2): 330–343
Kingsbury N G. Design of Q-shift complex wavelets for image processing using frequency domain energy minimization. In: The IEEE International Conference on Image Processing (ICIP’03), 2003. 1013–1016
Antoni J. Fast computation of the kurtogram for the detection of transient faults. Mech Mech Syst Signal Pr, 2007, 21(1): 108–124
Geronimo J S, Hardin D P, Massopust P R. Fractal functions and wavelet expansions based on several scaling functions. J Approx Theory, 1994, 78: 373–401
Wang X D. The research on early fault and composite fault diagnosis of machinery based on multiwavelet transform. Dissertation for the Doctoral Degree. Xi’an: Xi’an Jiaotong University, 2009
Antoni J. The spectral kurtosis: A useful tool for characterising non-stationary signals. Mech Syst Signal Pr, 2006, 20(2): 282–307
Lei Y G, Lin J, He Z J, et al. Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mech Syst Signal Process, 2011, 25(5): 1738–1749
Kovacevic J, Chebira A. Life beyond bases: The advent of frames (Part I). IEEE Signal Proc Mag, 2007, 24(4): 86–104
Qin Y, Tang B P, Wang J X. Higher-density dyadic wavelet transform and its application. Mech Syst Signal Pr, 2004, 24(3): 823–834
Chui C K, He W J. Compactly supported tight frames associated with refinable functions. Appl Comput Harmon A, 2000, 8(3): 293–319
Weickert T, Benjaminsen C, Kiencke U. Analytic wavelet packets-combining the dual-tree approach with wavelet packets for signal analysis and filtering. IEEE T Signal Proces, 2009, 57: 493–502
Selesnick I W, Abdelnour A F. Symmetric wavelet tight frames with two generators. Appl Comput Harmon A, 2004, 17(2): 211–225
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Chen, B., Zhang, Z., Zi, Y. et al. A pseudo wavelet system-based vibration signature extracting method for rotating machinery fault detection. Sci. China Technol. Sci. 56, 1294–1306 (2013). https://doi.org/10.1007/s11431-013-5139-z
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DOI: https://doi.org/10.1007/s11431-013-5139-z