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

By Kimon P. Valavanis

This publication represents the paintings of most sensible scientists within the box of clever keep an eye on and its functions, prognostics, diagnostics, dependent upkeep and unmanned structures. The paintings provides an method of fixing engineering difficulties relating to production, automation, and particularly unmanned structures and describes contemporary advances within the disciplines pointed out above. the most target of the publication is to illustrate how strategies and concepts from diversified disciplines are merged inside a standard framework utilized to the answer of complicated problems.

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Liu and R. Chen, Sequential Monte Carlo methods for dynamical systems, Journal for American Statistical Association 93, 1032–1044, 1998. 15. L. , Prentice-Hall, New Jersey, 1999. 16. A. V. M. V. A. Feldkamp and D. Roller, Applications of neural networks to the construction of virtual sensors and model-based diagnostics, in Proceedings of ISATA 29th International Symposium on Automotive Technology and Automation, 3–6 June, pp. 133–138, 1996. 17. L. Minsky, Step toward artificial intelligence, Proceedings IRE 49, 8–30, 1961.

For CBM of many mechanical systems, fault diagnosis and failure prognosis based on vibration signal analysis are essential techniques. Noise originating from various sources, however, often corrupts vibration signals and degrades the performance of diagnostic and prognostic routines, and consequently, the performance of CBM. In this paper, a new de-noising structure is proposed and applied to vibration signals collected from a testbed of the main gearbox of a helicopter subjected to a seeded fault.

2008 IEEE. P. ), Applications of Intelligent Control to Engineering Systems, 23–35. 24 M. Orchard et al. 1 Introduction Uncertainty management of prognostics holds the key for a successful penetration of prognostics as a key enabler to health management in industrial applications. While techniques to manage the uncertainty in the many factors contributing to current health state estimation – such as signal-to-noise ratio (SNR) on diagnostic features, optimal features with respect to detection statistics and ambiguity set minimization – have received a fair amount of attention due to the maturity of the diagnostics domain, uncertainty management for prognostics is an area which still awaits significant advances.

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