Machine fault diagnosis based on Frequency-Domain Blind Deconvolution

Proceedings of 2011 International Conference on Modelling, Identification and Control, ICMIC 2011

Volumn , Issue , 2011, Pages 63-68

  Pan, Nan ^a   Wu, Xing ^a   Chi, Yilin ^a   Liu, Xiaoqin ^a   Liu, Chang ^a  

^a KUNMING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC SIGNALS; BLIND DECONVOLUTION; BLIND SEPARATION ALGORITHMS; COMPLEX ENVIRONMENTS; EIGENVALUE DECOMPOSITION; FILTER COEFFICIENTS; FREQUENCY DOMAINS; KEY TECHNIQUES; MACHINE FAULT DIAGNOSIS; MACHINERY CONDITION MONITORING; MECHANICAL SIGNALS; MECHANICAL STRUCTURES; MODIFIED DISCRETE FOURIER TRANSFORMS; NONLINEAR FUNCTIONS; SPECTRUM METHOD;

ALGORITHMS; CONDITION MONITORING; CONVOLUTION; DISCRETE FOURIER TRANSFORMS; DOMAIN DECOMPOSITION METHODS; EIGENVALUES AND EIGENFUNCTIONS; FEATURE EXTRACTION; IDENTIFICATION (CONTROL SYSTEMS); MACHINERY; SIGNAL PROCESSING; VIBRATIONS (MECHANICAL);

FREQUENCY DOMAIN ANALYSIS;

EID: 80052091599     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (44)

1. Y. Wang, Y. L. Chi, X. Wu, et al., "Extracting Acoustical ImpulseSignal of Faulty Bearing Using Blind Deconvolution Method, " 2009Second International Conference on Intelligent ComputationTechnology and Automation; 2009 October 10-11: Changsha, China. United States: IEEE Computer Society 2009, pp. 590-594.
2. J. B. Wu, J. Chen, P. Zhong, "Application of the blind source separationmethod to feature extraction of machine sound signals, " Int J AdvManuf Technol, vol. 28 2006, pp. 855-862.
3. T. D. Popescu, "Blind separation of vibration signals and sourcechange detection-Application to machine monitoring, " AppliedMathematical Modeling, vol. 34, no. 11, 2010, pp. 3408-3421.
4. Z. C. Huang, J. F. Zhang, Faulty impulse signals extraction anddiagnosis of rolling element bearing: a blind deconvolution method, Journal of Vibration and Shock 25, vol. 34, no. 11, 2006, pp. 150-154.
5. T. T. Liu, X. M Ren, F. Guo, and Y. F. Yang, "Blind separation methodbased on second-order statistical for convolutive mixtures ofnon-stationary mechanical vibration signal, " Journal of MechanicalStrength, vol. 31, no. 6, 2009, pp. 900-904.
6. W. G. Huang, S. Y. Wu, F. R. Kong, et al, "Extracting AcousticalImpulse Signal of Faulty Bearing Using Blind Deconvolution Method, "2009 International Conference on Measuring Technology andMechatronics Automation; 2009 April 11-12: Zhangjiajie, China, pp. 598-601.
7. C. Wang, Y. Fong, Q. Zhang, "Frequency Domain Convolutive BlindSeparation Based on Modified Discrete Fourier Transform, " Journal ofData Acquisition & Processing, vol. 24, no. 5, 2009, pp. 556-562.
8. J. Liu, J. Xin, Y. Y. Qi, "A permutation alignment algorithm based onmultiple criterions in audio signal Frequency-Domain Blinddeconvolution, " Signal Processing (ICSP 2008), pp. 521-532.
9. L. Parra, C. Spence, "Convolutive blind separation of non-stationarysources, " IEEE Transactions on Speech and Audio Processing, vol. 8, no. 3, 2000, pp. 320-327.
10. P. Smaragdis, "Blind Separation of Convolved Mixtures in theFrequency Domain, " Neurocomputing, vol. 22, no. 1-3, 1998, pp. 21-34. (Pubitemid 28539487)
11. P. Xie, S. L. Grant, "A Fast and Efficient Frequency-Domain Methodfor Convolutive Blind Source Separation, " Region 5 Conference, IEEE, 2008, pp. 1-4.
12. E. F. Wang, N. T. Zhang, W. X. Meng, et al., "Novel compositoralgorithm based on neighbour frequency breadth-angle ratio infrequency domain BSS, " Computer Engineering and Applications , vol. 45, 2009, pp. 134-136.
13. T. Adali, T. Kim, V. Calhoun, "Independent component analysis bycomplex nonlinearities, " Acoustics, 2004 Speech, and SignalProcessing (ICASSP '04). pp. 525-528.
14. E. Moreau, "An any order generalization of JADE for complex sourcesignals, " 2001 Speech, and Signal Processing (ICASSP '01), pp. 2805-2806.
15. J. Liu, J. Xin, Y. Y. Qi, "A permutation alignment algorithm based onmultiple criterions in audio signal Frequency-Domain Blinddeconvolution, " Signal Processing (ICSP 2008), pp. 521-532.
16. H. Sawada, R. Mukai, S. Araki, and S. Makino, "A polar-coordinatebased activation function for frequency domain blind sourceseparation, " in Proc. ICASSP, Orlando, FL, 2002 May, pp. I-1001-I-1004.
17. X. Lan, Y. G. Zhang, S. A. Kassam, "Robust source separation usingranks, " Proceedings of the Tenth IEEE Workshop on Statistical Signaland Array Processing, 2000, pp. 324-328.
18. E. Bingham, "A. Hyvarinen, A fast fixed-point algorithm forindependent component analysis of complex-valued signals, " Int. J. Neural Systems, vol. 10, 2000, 1, pp. 1-8.
19. S. C. Douglas, H. Sawada, and S. Makino, "A spatio-temporal FastICAalgorithm for separating convolutive mixtures, " Proc. IEEE Int. Conf. Acoustic, Speech, Signal Processing, Mar. 2005, Philadelphia, PA, pp. 165-168.
20. Z. Koldovsky and P. Tichavsky, "Blind Instantaneous Noisy MixtureSeparation with Best Interference-Plus-Noise Rejection, " LectureNotes in Computer Science 4666, 2007, pp. 730-737.
21. V. Calhoun, T. Adali, "Independent component analysis ofcomplex-valued functional magnetic resonance imaging data bycomplex nonlinearities, " 2004 IEEE International Symposium onBiomedical Imaging: Nano to Macro, 2004 April 15-18, pp. 984-987. (Pubitemid 40514103)
22. J. Anemüller, T. J. Sejnowski, S. Makeig, "Complex independentcomponent analysis of frequency-domain electroencephalographicdata, " Neural Networks 16 (2003), pp. 1311-1323. (Pubitemid 37386370)
23. F. Y. Cong, J. Y. Lei, H. X Xu, et al, "Online Complex IndependentComponent Analysis Algorithm, " Journal of Shanghai JiaotongUniversity, vol. 41, no. 6, 2007, pp. 907-910.
24. M. G. Jafari, J. A. Chambers, "Wavelet domain natural gradientalgorithm for blind source separation of non-stationary sources, "Electronics Letters, vol. 38, no. 15, 2002, pp. 759-761. (Pubitemid 34806643)
25. H. W. Lou, G. R Hu, Adaptive wavelet domain algorithm for blindseparation of non-stationary convolved speech mixtures, Control andDesign, vol. 19, no. 1, 2004, pp. 73-76.
26. L. P. Belley, M. Gabrea, et al. "Continuous wavelet transform basedsource separation, " 50th Midwest Symposium on Circuits and Systems, 2007, pp. 365-368.
27. K. Rahbar, J. P. Reilly, "A Frequency Domain Method for Blind SourceSeparation of Convolutive Audio Mixtures, " IEEE Transactions onAudio, Speech and Language Processing vol. 13, 2005, pp. 832-844. (Pubitemid 41558899)
28. P. Xie, S. L. Grant, "A Fast and Efficient Frequency-Domain Methodfor Convolutive Blind Source Separation, " Region 5 Conference, 2008IEEE, pp. 1-4.
29. J. J. Zhu, H. G. Wang, H. X. Li, "An algorithm for solving permutationproblem based on the magnitude information received by microphonesin the frequency-domain BSS, " Technical Acoustics, vol. 27, no. 4, 2008, 593-596.
30. C. Wang, D. L. Peng, "Blind Source Separate of Mechanical Noisebased on Wavelet Transform, " Tool Engineering, vol. 42, no. 3, 2008, pp. 41-45.
31. W. D. Jiang, J. R. Lu, H. T. Zhang, et al., "Blind source separation ofspeech signals based on the amplitude correlation of neighbour bins, Journal of Circuits and Systems, vol. 10, no. 3, 2005, pp. 1-4.
32. F. Nesta, M. Omologo, "Multiple TDOA estimation by using a statecoherence transform for solving the permutation problem infrequency-domain BSS, " IEEE Workshop on Machine Learning forSignal Processing; 2008 October 16-19, pp. 43-48.
33. V. G. Reju, et al. "Partial separation method for solving permutationproblem in frequency domain blind source separation of speechsignals, " Neurocomputing, vol. 71, no. 10-12, 2008, pp. 2098-2112.
34. C. Zhang, X. P. Wu, "A Limitations of Frequency-Domain BlindDeconvolution and an Improved Algorithm, " Computer Technologyand Development, vol. 18, no. 10, 2008, pp. 57-60.
35. H. X. Xu, F. Y. Cong, X. Z. Shi, "Frequency Domain Multi-channelBlind Deconvolution by Complex Infomax, " Journal of DataAcquisition & Processing, vol. 23, no. 3, 2008, pp. 278-283.
36. W. Y. Wu, L. M. Zhang, Approach to Permutation Alignment of BlindSource Separation in Frequency-Domain for Convolutive MixingSpeech, Journal of Data Acquisition & Processing, vol. 23, no. 6, 2008, p. 734-739.
37. R. Mazur, A. Mertins, "An Approach for Solving the Permutation Problem of Convolutive Blind Source Separation Based on StatisticalSignal Models, " IEEE Transactions on Audio, Speech and LanguageProcessing, vol. 17, no. 1, 2009, pp. 117-126.
38. W. H. Wang, F. G. Huang, T. Wang, Improved Algorithm to EliminatePermutation Indetermination of Frequency Domain Blind SourceSeparation, Journal of System Simulation, vol. 21, no. 2, 2009, pp. 496-499.
39. W. Wang, H. Zhao, Q. Li, Z. X. Liu, "Engineering Signals' BlindSource Separation in Frequency Domain and Its Application, "International Conference on Natural Computation, 2009 August 14-16, pp. 537-541.
40. J. W. Li, C. X. Li, "Noise Separation Based on Blind Deconvolution inFrequency Domain, " Journal of Vibration and Shock, vol. 25, no. 6, 2006, pp. 100-103.
41. B. Zhang, et al, "Blind Deconvolution Denoising for HelicopterVibration Signals, " IEEE/ASME Transactions on Mechatronics, vol. 13, no. 5, 2008, pp. 558-310.
42. B. Zhang, et al, Application of Blind Deconvolution Denoising inFailure Prognosis, IEEE Transactions on Instrumentation andMeasurement, vol. 58, no. 2, 2009, pp. 303-310.
43. Y. L. Liu, H. Q. Zheng, Y. H. Li, "Blind Source Separation (BSS) ofGearbox Signal Based on Blind Deconvolution in the FrequencyDomain, " Science Technology and Engineering, vol. 9, no. 23, 2009, pp. 7002-7006.
44. H. Yang, Y. B. Liu, H. W. An, et al., "Turbine Vibration SourceSeparation Based on Independent Component Analysis, " InternationalConference on Mechatronics and Automation, 2010, pp. 1728-1731.