The article “Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data” (Alexis Sardá-Espinosaa, Subanatarajan Subbiah, Thomas Bartz-Beielstein), which will be published in the journal “Engineering Applications of Artificial Intelligence“, can be freely downloaded until May 20, 2017 from
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Abstract: Computational tools for the analysis of data gathered by monitoring systems are necessary because the amount of data steadily increases. Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner.
Keywords: Decision tree; Conditional inference tree; Health condition monitoring; Machine learning; Knowledge extraction
Authors: Alexis Sardá-Espinosa (ABB AG German Research Center, Technische Hochschule Köln), Subanatarajan Subbiah (ABB AG German Research Center), Thomas Bartz-Beielstein (Technische Hochschule Köln)
Here is the DOI: 10.1016/j.engappai.2017.03.008