Abstract
We present a robust algorithm for sequential imbalance detection (detecting a change of properties) for random processes with a wavelet packet transform. Based on this detector and artificial neural networks, we develop a classification system for different types of imbalance. We compare the resulting system with Shewhart control charts. The resulting system can be successfully used in selective control and under other conditions of imbalance detection and classification related to insufficient information about the signal before and after the change.
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References
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Original Russian Text © A.V. Gerasimov, Yu.V. Vasil’kov, 2011, published in Avtomatizatsiya v Promyshlennosti, 2011, No. 1, pp. 25–28.
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Gerasimov, A.V., Vasil’kov, Y.V. Imbalance detection and classification system based on wavelet analysis and artificial neural networks. Autom Remote Control 74, 1883–1889 (2013). https://doi.org/10.1134/S0005117913110106
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DOI: https://doi.org/10.1134/S0005117913110106