Decision Support in Intelligent Maintenance-planning Systems Based on Contextual Multi-armed Bandit Algorithm

A. Savchenko; Milov V. R.

doi:10.1016/j.procs.2017.01.114

Publications

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Decision Support in Intelligent Maintenance-planning Systems Based on Contextual Multi-armed Bandit Algorithm

P. 316–323.

Savchenko A., Milov V. R.

In this paper we focus on two essential problems of maintenance decision support systems, namely, 1) detection of potential dangerous situation, and 2) classification of this situation in order to recommend an appropriate repair action. The former task is usually solved with the known statistical process control techniques. The latter problem can be reduced to the contextual multi- armed bandit problem. We propose a novel algorithm with Bayesian classification of abnormal situation and the softmax rule to explore the decision space. The dangerous situations are detected with the Shewhart control charts for the distances between the current and the normal situations. It is experimentally shown, that our algorithm is more accurate than the known contextual multi-armed methods with stochastic search strategies.

Language: English

DOI

Keywords: Statistical Process Control contextual multi-armed bandit Pattern classification Maintenance decision support systems

In book

Advances in Information and Communication Technology: Proceedings of 7th International Congress of Information and Communication Technology (ICICT2017). Procedia Computer Science

Т. 107. , Амстердам: Elsevier, 2017.

Classification of emotion categories based on functional connectivity patterns of the human brain

Saarimäki H., Glerean E., Smirnov D. et al., Neuroimage 2022 No. 247 P. 1–10

Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) ...

Added: August 30, 2023

Classification of Dangerous Situations for Small Sample Size Problem in Maintenance Decision Support Systems

Milov V., Savchenko A., , in: 5th Conference on Analysis of Images, Social Networks, and Text (AIST 2016).: Springer, 2017. P. 338–345.

In this paper we examine the maintenance decision support in classification of the dangerous situations discovered by the monitoring system. This task is reduced to the contextual multi-armed bandit problem. We highlight small sample size problem appeared in this task due to the rather rare failures. The novel algorithm based on the nearest neighbor search ...

Added: October 26, 2018

Decision Support in Intelligent Maintenance-planning Systems Based on Contextual Multi-armed Bandit Algorithm

Savchenko A., Milov V., Procedia Computer Science 2017 Vol. 103 P. 316–323

Added: February 8, 2017

Pattern Classification with the Probabilistic Neural Networks Based on Orthogonal Series Kernel

Savchenko A., , in: Advances in Neural Networks – ISNN 2016. 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, ProceedingsТ. 9719: Lecture Notes in Computer Science.: Switzerland: Springer, 2016. P. 505–512.

Probabilistic neural network (PNN) is the well-known instance-based learning algorithm, which is widely used in various pattern classification and regression tasks, if rather small number of instances for each class is available. The known disadvantage of this network is its insufficient classification computational complexity. The common way to overcome this drawback is the reduction techniques ...

Added: September 16, 2016

The Adaptive Approach to Abnormal Situations Recognition Using Images from Condition Monitoring Systems

Savchenko A., Milov V., Optical Memory and Neural Networks (Information Optics) 2016 Vol. 25 No. 2 P. 79–87

Decision support in equipment condition monitoring systems with image processing is analyzed. Long-run accumulation of information about earlier made decisions is used to realize the adaptiveness of the proposed approach. It is shown that unlike conventional classification problems, the recognition of abnormalities uses training samples supplemented with reward estimates of earlier decisions and can be ...

Added: July 10, 2016

Interpretation of Shewhart Control Charts: New Challenges and Opportunities

Maksimova O., Adler Y., Shper V., Communications in Dependability and Quality Management 2014 No. V. 17, N. 1 P. 23–29

This work shows that an interpretation of signals on Shewhart control charts (SCC) can be different from commonly used as soon as one takes into account that an assignable cause of variation can change not only the parameters of the underlying distribution but its type as well. For example, the lack of points beyond 3σ ...

Added: December 29, 2014