By Kimon P. Valavanis

ISBN-10: 9048130174

ISBN-13: 9789048130177

This booklet represents the paintings of most sensible scientists within the box of clever regulate and its functions, prognostics, diagnostics, situation established upkeep and unmanned platforms. The paintings provides an method of fixing engineering difficulties regarding production, automation, and particularly unmanned structures and describes contemporary advances within the disciplines pointed out above. the most objective of the booklet is to illustrate how innovations and concepts from diversified disciplines are merged inside of a standard framework utilized to the answer of advanced problems.

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Additional info for Applications of Intelligent Control to Engineering Systems: In Honour of Dr. G. J. Vachtsevanos

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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. 18. D. Muench, G. Kacprzynski, A. Liberson, A. Sarlashkar and M. Roemer, Model and sensor fusion for prognosis, Example: Kalman filtering as applied to corrosion-fatigue and FE models, SIPS Quarterly, Review Presentation, 2004.

A practical solution is to focus on the main vibration source that contributes mostly to the vibration, while it treats all other sources as a combined noise. Then, the objective is reduced to separating the vibration source from noise, which, in this sense, is the cu- 40 B. Zhang et al. mulative contribution of many different sources. This leads to a blind deconvolution de-noising algorithm [17–19]. Previous research work reported in [9] has provided a good understanding of the true vibration signals, originating from the epicyclic gearbox under both healthy and faulty operational conditions.

1 Prognostic Methods with Application to an Integrated Health Management System 19 Fig. 14 Distributed prognostic system architecture. 12 Conclusions This chapter has briefly reviewed some generic prognosis algorithmic approaches and introduced the theoretical and notional foundations associated with probabilistic predictions and required software architectures for performing prognostics on critical aerospace systems. Prognosis is certainly the Achilles’ heel of the Prognostics and Health Management (PHM) system and presents major challenges to the CBM/PHM system designer primarily because it entails large-grain uncertainty.

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Applications of Intelligent Control to Engineering Systems: In Honour of Dr. G. J. Vachtsevanos by Kimon P. Valavanis


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