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Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data

  • As the amount of data gathered by monitoring systems increases, using computational tools to analyze it becomes a necessity. 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

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Metadaten
Author:Alexis Sardá-Espinosa, Subanatarajan Subbiaha, Thomas Bartz-BeielsteinGND
URN:urn:nbn:de:hbz:832-cos4-4709
Series (Serial Number):CIplus (1/2017)
Document Type:Working Paper
Language:English
Year of Completion:2017
Release Date:2017/04/20
Tag:Conditional inference tree; Health condition monitoring; Knowledge extraction; Machine learning
Decision tree
Pagenumber:16
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung