Probabilistic kernel machines for predictive monitoring of weld residual stress in energy systems

Author:

Alamaniotis Miltiadis,Mathew Jino,Chroneos Alexander,Fitzpatrick Michael E.,Tsoukalas Lefteri H.

Funder

Lloyd’s Register Foundation

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference59 articles.

1. Prediction of residual stresses in resistance spot weld;Afshari;Aircr. Eng. Aerosp. Technol.: Int. J.,2016

2. Regression to fuzziness method for estimation of remaining useful life in power plant components;Alamaniotis;Mech. Syst. Signal Process.,2014

3. Alamaniotis, M., Ikonomopoulos, A., Tsoukalas, L.H., 2010. Gaussian processes for failure prediction of slow degradation components in nuclear power plants. In: Proceedings of the European Safety and Reliability Conference 2010, ESREL 2010, Rhodes, Greece, September, pp. 2096–2102.

4. Probabilistic kernel approach to online monitoring of nuclear power plants;Alamaniotis;Nucl. Technol.,2012

5. Alamaniotis, M., Tsoukalas, L.H., Ikonomopoulos, A., 2011. On-line surveillance of nuclear power plant peripheral components using support vector regression. In: Proceedings of the International Symposium on Future I&C for Nuclear Power Plants, Cognitive Systems Engineering on Process Control, and International Symposium on Symbiotic Nuclear Power Systems, ICI 2011, Daejeon, Korea, August 2011, pp. 1230(1–6).

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