Time-frequency manifold for gear fault signature analysis

Conference Record - IEEE Instrumentation and Measurement Technology Conference

Volumn , Issue , 2011, Pages 491-495

  He, Qingbo ^a   Liu, Yongbin ^a   Wang, Jun ^a   Wang, Jianjun ^a   Gong, Chang ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

gear; machine health diagnosis; manifold learning; phase space reconstruction; time frequency distribution

Indexed keywords

GEAR FAULTS; HIGH DIMENSIONAL PHASE SPACE; INTRINSIC FEATURES; MACHINE HEALTH; MANIFOLD ANALYSIS; MANIFOLD LEARNING; NONLINEAR INFORMATION; NONLINEAR MANIFOLDS; NONSTATIONARY; NONSTATIONARY SIGNALS; PHASE SPACE RECONSTRUCTION; TIME FREQUENCY; TIME FREQUENCY ANALYSIS; TIME FREQUENCY FEATURES; TIME-FREQUENCY DISTRIBUTIONS;

ADAPTIVE FILTERING; CONDITION MONITORING; INSTRUMENTS; MEASUREMENT THEORY; PHASE SPACE METHODS; ROBUSTNESS (CONTROL SYSTEMS);

HEALTH;

EID: 80051891331     PISSN: 10915281     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IMTC.2011.5944226     Document Type: Conference Paper

References (12)

1. J. D. Jiang, J. Chen, and L. S. Qu, "The application of correlation dimension in gearbox condition monitoring", J. Sound Vib., vol. 23, pp. 529-542, 1999.
2. Z. Feng, M. J. Zuo, and F. Chu, "Application of regularization dimension to gear damage assessment", Mech. Syst. Signal Process., vol.24, no. 4, pp. 1081-1098, 2010.
3. R. Yan and R.X. Gao, "Complexity as a measure for machine health evaluation", IEEE Trans. Instrum. Meas., vol. 53, pp. 1237-1334, 2004.
4. G. F. Wang, Y. B. Li, and Z. G. Luo, "Fault classification of rolling bearing based on reconstructed phase space and Gaussian mixture model", J. Sound Vib., vol. 323, pp. 1077-1089, 2009.
5. Q. He, F. Kong, and R. Yan, "Subspace-based gearbox condition monitoring by kernel principal component analysis", Mech. Syst. Signal Process., vol. 21, no.4, pp. 1755-1772, 2007.
6. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding", Science, vol. 290, no. 5500, pp. 2323-2326, 2000.
7. J. B. Tenenbaum, V. Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction", Science, vol. 290, no. 5500, pp. 2319-2323, 2000.
8. Z. Zhang and H. Zha, "Principal manifolds and nonlinear dimensionality reduction via tangent space alignment", SIAM Journal of Scientific Computing, vol. 26, no. 1, pp. 313-338, 2005.
9. M. Li, J. Xu, J. Yang, D. Yang, and D. Wang, "Multiple manifolds analysis and its application to fault diagnosis", Mech. Syst. Signal Process., vol. 23, no. 8, pp. 2500-2509, 2009.
10. Q. Jiang, M. Jia, J. Hu, and F. Xu, "Machinery fault diagnosis using supervised manifold learning", Mech. Syst. Signal Process., vol. 23, no. 7, pp. 2301-2311, 2009.
11. I. Lopez and N. Sarigul-Klijn, "Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studies", Mech. Syst. Signal Process., vol. 23, no. 7, pp. 2287-2300, 2009.
12. L. Cao, "Practical method for determining the minimumem bedding dimension of a scalar time series", Physics D, vol. 110, pp. 43-50, 1997.