Researchers from Aalto University Describe Findings in Machine Learning (Generat ion of Unmeasured Loading Levels Data for Condition Monitoring of Induction Mach ine Using Machine Learning)
Researchers from Aalto University Describe Findings in Machine Learning (Generat ion of Unmeasured Loading Levels Data for Condition Monitoring of Induction Mach ine Using Machine Learning)
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Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Espoo, Finland, by NewsRx journalist s, research stated, “This article presents a novel data augmentation method that generates feature values for unmeasured loading levels based on limited measure d and simulated loading level data. The incorporation of offline simulated data in the augmentation framework and the mapping of the error distribution over the loading levels greatly reduce the dependency on including a large number of loa ding levels in the curve fitting process.” Financial support for this research came from Academy of Finland Consortium. The news correspondents obtained a quote from the research from Aalto University , “Furthermore, the proposed method shows high potential to minimize the deviati on between measured and simulated data at the feature level. The method is appli ed to the induction machine (IM) to generate feature values at 25% and 50% loading levels for healthy, one, two, and three broken rot or bars (BRBs) conditions. An excellent agreement is observed between the augmen ted and actual feature values calculated from the measured data at 25% and 50% loading levels.”
Key words
Espoo, Finland, Europe, Cyborgs, Emergin g Technologies, Machine Learning, Aalto University
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