Abstract
In this article, the detection of a fault on the inner race of a roller bearing is presented as a problem of optimal estimation of a hidden fault, via measures delivered by a vibration sensor. First, we propose a linear model for the transmission of a vibratory signal to the sensor’s diaphragm. The impact of shocks due to the default is represented by a stochastic drift term whose values are in a discrete set. To determine the state of the roller bearing, we estimate the value of this term using particular filtering.
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References
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Bennis, O., Kratz, F. (2010). Deterministic Particle Filtering and Application to Diagnosis of a Roller Bearing. In: Solé-Casals, J., Zaiats, V. (eds) Advances in Nonlinear Speech Processing. NOLISP 2009. Lecture Notes in Computer Science(), vol 5933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11509-7_22
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DOI: https://doi.org/10.1007/978-3-642-11509-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11508-0
Online ISBN: 978-3-642-11509-7
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