Butler, Shane and Ringwood, John
Particle Filters for Remaining Useful Life Estimation of Abatement
Equipment used in Semiconductor Manufacturing.
In: 2010 Conference on Control and Fault Tolerant Systems, October 6-8, 2010, Nice, France.
Prognostics is the ability to predict the remaining
useful life of a specific system, or component, and represents
a key enabler of any effective condition-based-maintenance
strategy. Among methods for performing prognostics such as
regression and artificial neural networks, particle filters are
emerging as a technique with considerable potential. Particle
filters employ both a state dynamic model and a measurement
model, which are used together to predict the evolution of
the state probability distribution function. The approach has
similarities to Kalman filtering, however, particle filters make
no assumptions that the state dynamic model be linear or that
Gaussian noise assumptions must hold true.
The technique is applied in predicting the degradation of
thermal processing units used in the treatment of waste gases
from semiconductor processing chambers. The performance of
the technique demonstrates the potential of particle filters as a
robust method for accurately predicting system failure.
In addition to the use of particle filters, Gaussian Mixture
Models (GMM) are employed to extract signals associated
with the different operating modes from a multi-modal signal
generated by the operating characteristics of the thermal
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