scholarly journals Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 185 ◽  
Author(s):  
Nicolas Dupin ◽  
El-Ghazali Talbi
Keyword(s):  
Machine Learning ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Machine Learning Techniques ◽  
Mixed Integer ◽  
Planning Problem ◽  
Maintenance Planning ◽  
Learning Techniques ◽  
Dual Bounds

This paper studies the hybridization of Mixed Integer Programming (MIP) with dual heuristics and machine learning techniques, to provide dual bounds for a large scale optimization problem from an industrial application. The case study is the EURO/ROADEF Challenge 2010, to optimize the refueling and maintenance planning of nuclear power plants. Several MIP relaxations are presented to provide dual bounds computing smaller MIPs than the original problem. It is proven how to get dual bounds with scenario decomposition in the different 2-stage programming MILP formulations, with a selection of scenario guided by machine learning techniques. Several sets of dual bounds are computable, improving significantly the former best dual bounds of the literature and justifying the quality of the best primal solution known.

scholarly journals Using Machine Learning for Decreasing State Uncertainty in Planning

2020 ◽  
Vol 69 ◽  
pp. 765-806
Author(s):  
Senka Krivic ◽  
Michael Cashmore ◽  
Daniele Magazzeni ◽  
Sandor Szedmak ◽  
Justus Piater
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Process ◽  
The State ◽  
Machine Learning Techniques ◽  
Planning Problem ◽  
Planning Techniques ◽  
Novel Approach ◽  
Learning Techniques ◽  
State Uncertainty

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.

scholarly journals INTELLIGENT TECHNIQUES FOR ANOMALY DETECTION IN NUCLEAR REACTORS

2021 ◽  
Vol 247 ◽  
pp. 21011
Author(s):  
George Ioannou ◽  
Thanos Tagaris ◽  
Georgios Alexandridis ◽  
Andreas Stafylopatis
Keyword(s):  
Signal Processing ◽  
Neutron Flux ◽  
Nuclear Power ◽  
Power Plants ◽  
Reactor Core ◽  
Machine Learning Techniques ◽  
Full Potential ◽  
Pressurized Water ◽  
Learning Techniques ◽  
Water Reactors

The safe operation of nuclear power plants is highly dependent on the ability of quickly and accurately identifying possible anomalies and perturbations in the reactor. Operational defects are primarily diagnosed by detectors that capture changes in the neutron flux, placed at various points inside and outside of the core. Neutron flux signals are subsequently analyzed with signal processing techniques in an effort to be better described (have their higher-order characteristics uncovered, locate transient events, etc). To this end, the application of intelligent techniques may be extremely beneficial, as it may assist and extend the current level of analysis. Besides, the combination of signal processing methodologies and machine learning techniques in the framework of nuclear power plant data is an emerging topic that has yet to show its full potential. In this context, the current contribution attempts at introducing intelligent approaches and more specifically, deep learning techniques, in neutron flux signal analysis for the identification of perturbations and other anomalies in the reactor core that may affect its operational capabilities. The obtained results of an initial stage of analysis on neutron flux signals captured at pressurized water reactors are encouraging, underlying the robustness and the potential of the proposed approach.

scholarly journals Automatic Prediction of Maintenance Intervention Types in Roads using Machine Learning and Historical Records

2018 ◽  
Vol 2672 (44) ◽  
pp. 43-54 ◽  
Author(s):  
F. J. Morales ◽  
A. Reyes ◽  
N. Caceres ◽  
L. Romero ◽  
F. G. Benitez
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Road Network ◽  
Transport Infrastructure ◽  
Machine Learning Techniques ◽  
Maintenance Planning ◽  
Historical Records ◽  
Learning Techniques ◽  
Severity Levels ◽  
Maintenance Intervention

A methodology to support and automate the prediction of maintenance intervention alerts in transport linear-asset infrastructures can greatly aid maintenance planning and management. This paper proposes a methodology combining the current and predicted conditions of the assets, and unit components of the infrastructure, with operational and historical maintenance data, to derive information about maintenance interventions needed to avoid later severe degradation. By means of data analytics and machine learning techniques, the proposed methodology generates a prioritized listing, ranked on severity levels, corresponding to the pre-alerts and alerts generated for all assets of the transport infrastructure. The methodology is applied and tested in a real case consisting of a road network with different section classes. The analysis of the results shows that the algorithms and tools developed have good predicting capabilities.

Sign in / Sign up

Export Citation Format

Share Document