By Kimon P. Valavanis
This ebook displays the paintings of most sensible scientists within the box of clever keep watch over and its purposes, prognostics, diagnostics, situation dependent upkeep and unmanned platforms. It contains effects, and offers how conception is utilized to unravel genuine problems.
Read or Download Applications of Intelligent Control to Engineering Systems: In Honour of Dr. G. J. Vachtsevanos (Intelligent Systems, Control and Automation: Science and Engineering) PDF
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Extra resources for Applications of Intelligent Control to Engineering Systems: In Honour of Dr. G. J. Vachtsevanos (Intelligent Systems, Control and Automation: Science and Engineering)
3. Provides the ability to isolate and assess the extent of multiple faults and battle damage, hence improving survivability of the vehicle. 4. Hierarchical reasoners have a “built-in” data management capability for containing erroneous information and utilizing multiple data and information sources. 5. Ability to capture and localize system degradations (as opposed to only hard failures), based on increased health awareness of the lowest-level LRUs, hence providing a more accurate vehicle availability assessment.
2 Particle Filtering Applied to Prognostics 29 Fig. 3 Particle filtering-based uncertainty management system. Accuracy of long-term predictions is directly related to the estimates of xt and the model hyper-parameters that affect E[xt | xt −1 ]. Precision in long-term predictions, on the other hand, is directly related to the hyper-parameters that describe the variance of the noise structures considered in the state equation. 3, where the performance of the algorithm is evaluated in terms of the short-term prediction error (which depends on the PF-based pdf estimate).
J. Werbos, Generalization of back propagation with application to recurrent gas market model, Neural Networks 1, 339–356, 1988. Chapter 2 Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics∗ Marcos Orchard, Gregory Kacprzynski, Kai Goebel, Bhaskar Saha and George Vachtsevanos Abstract Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution.