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

Miltiadis Alamaniotis, Jino Mathew, Alexander Chroneos, Michael Fitzpatrick, Lefteri Tsoukolas

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
98 Downloads (Pure)


Predictive monitoring supports the a priori scheduling of critical component maintenance and contributes significantly in attaining a safe yet economic operation and management of complex energy systems by mitigating the risk of accidents and minimizing the number of operational pauses. The current work studies the learning ability of probabilistic kernel machines, and more particularly of Gaussian Processes (GP) equipped with various kernels for the estimation of weld residual stress profiles of stainless steel pipe welds. The GP models are tested on experimentally-obtained data of axial and hoop residual stresses in two different stainless-steel pipes. The results exhibit the ability of GP to accurately predict the weld residual stress profile in the axial and hoop direction by providing a predictive distribution, i.e., mean and variance values. Furthermore, performance of GP is compared to a non-probabilistic kernel machine, such as support vector regression (SVR) equipped with the same kernels, and to multivariate linear regression (MLR). Comparison results exhibit the robustness of GP over SVR and MLR with respect to prediction accuracy of weld residual stress in terms of root mean square error. With respect to a second metric, namely, correlation coefficient between measured and predicted values, GP is superior to SVR and MLR in the majority of the cases.
Original languageEnglish
Pages (from-to)138-154
Number of pages16
JournalEngineering Applications of Artificial Intelligence
Early online date20 Mar 2018
Publication statusPublished - May 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence, [71, (2018)] DOI: 10.1016/j.engappai.2018.02.009

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


  • Probabilistic kernel machines
  • Gaussian process regression
  • Machine learning
  • Welding
  • Residual stresses
  • Support vector regression


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