Probabilistic Latent Component Analysis for Gearbox Vibration Source Separation



Published Oct 10, 2010
Joshua Isom Madhusudana Shashanka Ashutosh Tewari Aleksandar Lazarevic


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

Isom, J., Shashanka, M., Tewari, A., & Lazarevic, A. (2010). Probabilistic Latent Component Analysis for Gearbox Vibration Source Separation. Annual Conference of the PHM Society, 2(1).
Abstract 91 | PDF Downloads 71



vibration, gearbox, source separation

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