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
Author: | Alexis Sardá-Espinosa, Subanatarajan Subbiaha, Thomas Bartz-BeielsteinGND |
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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 |
Page Number: | 16 |
Institutes and Central Facilities: | Fakultät für Informatik und Ingenieurwissenschaften (F10) |
Dewey Decimal Classification: | 000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
Open Access: | Open Access |
Licence (German): | Creative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung |