Applications of Intelligent Control to Engineering Systems: by Kimon P. Valavanis

By Kimon P. Valavanis

This publication displays the paintings of best scientists within the box of clever keep watch over and its purposes, prognostics, diagnostics, situation dependent upkeep and unmanned platforms. It comprises effects, and offers how conception is utilized to resolve genuine problems.

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Additional resources for Applications of Intelligent Control to Engineering Systems: In Honour of Dr. G. J. Vachtsevanos (Intelligent Systems, Control and Automation: Science and Engineering)

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Studer and F. Masulli, On the structure of a neuro-fuzzy system to forecast chaotic time series, in Proceedings of the International Symposium on Neuro-Fuzzy Systems, 29–31 August, pp. 103–110, 1996. 26. S. Sutton, Introduction: The challenge of reinforcement learning, Machine Learning 8, 225– 227, 1992. 27. J. Veaux, J. Schweinsberg and J. Ungar, Prediction intervals for neural networks via nonlinear regression, Technometrics 40(4), 273–282, November 1998. 28. P. Wang and G. Vachtsevanos, Fault prognostics using dynamic wavelet neural networks, Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15(4), 349–365, September 2001.

A reasonable modification of the nonlinear projection is given as follows. From previous research in [9], a vibration model in the frequency domain is established with the load profile and the crack size being two of the inputs. Note that the load profile is known and the crack size can be estimated from the prognostic algorithm [10]. Then, the modeled noise-free vibration signal m(t) generated from the vibration model is Fourier transformed in 48 B. Zhang et al. Fig. 11 The influence of crack size on frequency spectra.

4] used an RBFNN to obtain confidence limits for a prognosticator. Probabilistic reliability analysis tools employing an inner-outer loop Bayesian update scheme [5, 6] have also been used to “tune” model hyperparameters given observations. However, the scalability of this rigorous approach for more than a few parameters is unproven and relies on the assumption that all distributions are unimodal. This paper introduces a generic and systematic methodology to the uncertainty representation and management problem in failure prognosis by capitalizing on notions from Bayesian estimation theory and, specifically, particle filtering (PF) [7, 9–10] for long-term prognosis in non-linear dynamic systems with non-Gaussian noise, appropriate kernels to reduce the impact of model errors and feedback correction loops to improve the accuracy and precision of the remaining useful life estimates.

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Applications of Intelligent Control to Engineering Systems: by Kimon P. Valavanis
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