Fault feature extraction using independent component analysis with reference and its application on fault diagnosis of rotating machinery

Abstract In practical situations, the vibration collected from rotating machinery is often a mixture of many vibration components and noise; therefore, it is very necessary to extract fault features from the mixture first in order to achieve effective rotating machinery fault diagnosis. In this paper, independent component analysis with reference method is proposed to extract the fault features using reference signals established based on the a priori knowledge of machine faults; experimental studies based on both simulated and actual fault signals of rotating machinery have been performed; and the results show that the proposed approach can effectively extract fault features under the situation of interferences and coexistence of multiple faults.

[1]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[2]  Anton Gunzinger,et al.  Fast neural net simulation with a DSP processor array , 1995, IEEE Trans. Neural Networks.

[3]  DelftThe Netherlandsypma,et al.  Rotating Machine Vibration Analysis with Second-order Independent Component Analysis , 1999 .

[4]  G. Delaunay,et al.  BLIND SOURCES SEPARATION APPLIED TO ROTATING MACHINES MONITORING BY ACOUSTICAL AND VIBRATIONS ANALYSIS , 2000 .

[5]  Alexander Ypma,et al.  Learning methods for machine vibration analysis and health monitoring , 2001 .

[6]  Christopher J. James,et al.  Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis , 2003, IEEE Transactions on Biomedical Engineering.

[7]  S. Poyhonen,et al.  INDEPENDENT COMPONENT ANALYSIS OF VIBRATIONS FOR FAULT DIAGNOSIS OF AN INDUCTION MOTOR , 2003 .

[8]  Jing Lin,et al.  Gearbox fault diagnosis using independent component analysis in the frequency domain and wavelet filtering , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[9]  P. Fabry,et al.  Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic , 2005 .

[10]  J. Antoni.  Blind separation of vibration components: Principles and demonstrations , 2005 .

[11]  M. Zuo,et al.  Feature separation using ICA for a one-dimensional time series and its application in fault detection , 2005 .

[12]  C.J. James,et al.  On the use of Spectrally Constrained ICA applied to single-channel Ictal EEG recordings within a Dynamical Embedding Framework , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[13]  M. Zacksenhouse,et al.  A blind deconvolution separation of multiple sources, with application to bearing diagnostics , 2005 .

[14]  Wei Lu,et al.  Approach and applications of constrained ICA , 2005, IEEE Transactions on Neural Networks.

[15]  Wei Lu,et al.  ICA with Reference , 2006, Neurocomputing.

[16]  Shoko Araki,et al.  Geometrically Constrained Independent Component Analysis , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[17]  Fanrang Kong,et al.  Detection of signal transients using independent component analysis and its application in gearbox condition monitoring , 2007 .

[18]  Christopher J. James,et al.  Extracting Rhythmic Brain Activity for Brain-Computer Interfacing through Constrained Independent Component Analysis , 2007, Comput. Intell. Neurosci..

[19]  De-Shuang Huang,et al.  A New Constrained Independent Component Analysis Method , 2007, IEEE Transactions on Neural Networks.

[20]  Zhi-Lin Zhang,et al.  Morphologically constrained ICA for extracting weak temporally correlated signals , 2008, Neurocomputing.

[21]  D. P. Acharya,et al.  A Review of Independent Component Analysis Techniques and Their Applications , 2022 .