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            <idno type="halRefHtml">&lt;i&gt;8th European Workshop On Structural Health Monitoring (EWSHM 2016) &lt;/i&gt;, Jul 2016, Bilbao Spain</idno>
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                <term xml:lang="en"> GPUPU</term>
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                <term xml:lang="en"> Particular filtering</term>
                <term xml:lang="en">Vibration monitoring</term>
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              <p>This paper presents a method based on the use of Bayesian modal parameter recursive estimation basedon a particular Kalman filter algorithm with decoupled distributions for mass and stiffness. ParticularKalman filtering is a combination of two widely used Bayesian estimation methods working together:the particle filter (also called sequential Monte Carlo samplings) and the Kalman filter. Usual systemidentification techniques for civil and mechanical structures assume the availability of large set of dataderived from a stationary quasi steady structure. On the opposite, several scenarios involve time varyingstructures. For example, due to interaction with aerodynamics in aeronautics, some critical parametermay have to be monitored, for instability monitoring (leading possibly to flutter) of in flight data due tofuel consumption and speed change. This relates to the monitoring of time varying structural parameterssuch as frequencies and damping ratios. The main idea of a particular Kalman filter is to considerstochastic particles evolving in the parameter space. For each particle, a corresponding linear state isrecursively estimated by applying a Kalman filter to the mechanical system, whose modal parametersare driven by the evolution of this time-varying particle. The weight of each particle is computed fromthe likelihood of the parameter sample it represents and its corresponding state. This result in a bank ofadaptive coupled Kalman filters combined with their particle filter. However, the system parametrizationis relatively large. In order to provide fast and convincing results for large time varying structure, suchas an airplane, the execution time of the method has to be improved. In particular, the particle evolutionscan be run in parallel, Within the Cloud2sm project, A Quadro k6000 card of 3072 cores clocked to 3GB/s has been used. This paper will show a GPGPU implementation of the particular Kalman filter andthe first results will be discussed.</p>
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