Probabilistic Latent Component Analysis for Gearbox Vibration Source Separation
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Abstract
Probabilistic latent component analysis (PLCA) is applied to the problem of gearbox vibration source separation. A model for the probability distribution of gearbox vibration employs a latent variable intended to correspond to a particular vibration source, with the measured vibration at a particular sensor for each source the product of a marginal distribution of vibration by frequency, a marginal distribution of vibration by shaft rotation, and a sensor weight distribution. An expectation-maximization algorithm is used to approximate a maximum-likelihood parameterization for the model. In contrast to other unsupervised source-separation methods, PLCA allows for separation of vibration sources when there are fewer vibration sensors than vibration sources. Once the vibration components of a healthy gearbox have been identified, the vibration characteristics of damaged gearbox elements can be determined. The efficacy of the technique is demonstrated with an application on a gearbox vibration data set.
How to Cite
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vibration, gearbox, source separation
G. Gelle, M. Colas, C. Serviere (2003). Blind source separation: a new pre-processing tool for rotating machines monitoring. IEEE Transactions on Instrumentation and Measurement, vol. 52, pp. 790- .795
Huang, N.E., Shen, Z., Long, Sr., Wu, M.C., Shih, H.H, Zheng, Q., Yen, N.C., Tung, C.C., & Liu, H.H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, vol. 454, pp. 903-995.
de Leva, P (2005). Multiple matrix multiplications, with array expansion enabled, MATLAB Central File Exchange. Retrieved Aug 2010.
Neal, R. & Hinton, G.E. (1998). A View of EM algorithm that justifies incremental, sparse, and other variants, Learning in Graphical Models, pp .355-368
Serviere, C. & Fabry, P. (2004). Blind source separation of noisy harmonic signals for rotating machine diagnosis. Journal of Sound and Vibration, vol. 272, pp. 317-339.
Smaragdis, P. & Raj, B. (2007). Shift-Invariant Probabilistic Latent Component Analysis. Mitsubishi Ele. Res. Labs. Technical Report TR2007-009, 2007.
Smaragdis, P., Raj, B., & M. Shashanka (2007). “Supervised and semi-supervised separation of sounds from single-channel mixtures,” in Proceedings of the 7th International Conference on Independent Component Analysis and Blind Signal Separation (ICA ’07), pp. 414–421, London, UK.
Smaragdis, P., Raj, B., & Shashanka, M. (2006). “A probabilistic latent variable model for acoustic modeling,” in Proceedings of the Advances in Models for Acoustic Processing Workshop (NIPS ’06),Whistler, BC, Canada.
Stewart, R.M. (1977). Some Useful Data Analysis Techniques for Gearbox Diagnostics. Machine Health Monitoring Group, Institute of Sound and Vibration Research, University of Southhampton, Report MHM/R/10/77.
Zhong-shen, C. Yong-min, Y. & Guo-ji, S. (2004). Application of independent component analysis to early diagnosis of helicopter gearboxes. Mechanical Science and Technology, vol. 4.
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