Risk Estimation in Data-driven Fault Prediction for a Biomass-fired Power Plant

Ivan Ryzhikov, Mika Liukkonen, Ari Kettunen, Yrjö Hiltunen

2020

Abstract

In this study, we consider a fault prediction problem for the case when there are no variables by which we could determine that the system is in the fault state. We propose an approach that is based on constructing auxiliary variable, thus it is possible to reduce the initial problem to the supervised learning problem of risk estimation. The suggested target variable is an indicator showing how close the system is to the fault that is why we call it a risk estimation variable. The risk is growing some time before the actual fault has happened and reaches the highest value in that timestamp, but there is a high level of uncertainty for the times when the system has been operating normally. We suggest specific criterion that takes uncertainty of risk estimation into account by tuning three weighting coefficients. Finally, the supervised learning problem with risk variable and specific criterion can be solved by the means of machine learning. This work confirm that data-driven risk estimation can be integrated into digital services to successfully manage plant operational changes and support plant prescriptive maintenance. This was demonstrated with data from a commercial circulating fluidized bed firing various biomass and residues but is generally applicable to other production plants.

Paper Citation

in Harvard Style

Ryzhikov I., Liukkonen M., Kettunen A. and Hiltunen Y. (2020). Risk Estimation in Data-driven Fault Prediction for a Biomass-fired Power Plant. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA; ISBN 978-989-758-475-6, SciTePress, pages 423-429. DOI: 10.5220/0010113104230429

in Bibtex Style

@conference{ncta20,
author={Ivan Ryzhikov and Mika Liukkonen and Ari Kettunen and Yrjö Hiltunen},
title={Risk Estimation in Data-driven Fault Prediction for a Biomass-fired Power Plant},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA},
year={2020},
pages={423-429},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010113104230429},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: NCTA
TI - Risk Estimation in Data-driven Fault Prediction for a Biomass-fired Power Plant
SN - 978-989-758-475-6
AU - Ryzhikov I.
AU - Liukkonen M.
AU - Kettunen A.
AU - Hiltunen Y.
PY - 2020
SP - 423
EP - 429
DO - 10.5220/0010113104230429
PB - SciTePress