The adaptive approach to abnormal situations recognition using images from condition monitoring systems

Abstract

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 tackled using reinforcement learning algorithms. We consider the basic stages of contextual multi-armed bandit algorithms during which the probabilistic distributions of each state are evaluated to evaluate the current knowledge of the states, and the decision space is explored to increase the decision-making efficiency. We propose a new decision-making method, which uses the probabilistic neural network to classify abnormal situation and the softmax rule to explore the decision space. A modelling experiment in image processing was carried out to show that our approach allows a higher accuracy of abnormality detection than other known methods, especially for small-size initial training samples.