Squirrel-cage induction motor malfunction detection using computational intelligence methods
Authors:
- Krzysztof Rzecki,
- Bartosz Wójcik,
- Mateusz Baran,
- Maciej Sułowicz
Abstract
The squirrel-cage induction motors (commonly called just electric motors) are widely used in electromechanical devices. They usually act as a source of mechanical power for different types of industrial machines. There is a natural life cycle of such electric motors ending in malfunction caused by damage of particular electric or mechanical parts. Sudden and unforeseen engine failure may turn out to be a heavy cost for the company. Early detection of motor damage can minimize repair costs. In this work a machine-learning based methodology for early motor malfunction detection is presented. A test stand with a three-phase induction motor that can simulate various types of stator winding short-circuit faults under load controlled by a DC generator was build. This stand was equipped with multiple sensors for continuous monitoring. Readings from sensors were collected for different loads and types of damage. Multiple methods of preprocessing and classification were tested. Sensors types are evaluated for accuracy of malfunction recognition based on the results of computational experiments. The 5-fold stratified cross-validation was used for evaluation of preprocessing steps and classifiers. The best results were achieved for neutral voltage, axial flux, and torque sensors. Acquisition time of 0.16 s is sufficient for accurate classification.
- Record ID
- CUTe991283a4a3f4656886805df3472bbc7
- Publication categories
- ; ;
- Author
- Pages
- 673-684
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 683-684; Bibliografia (liczba pozycji) - 24; Oznaczenie streszczenia - Abstr.
- Book
- Rutkowski Leszek, Leszek Rutkowski Scherer Rafał, Rafał Scherer Korytkowski Marcin Marcin Korytkowski [et al.] (eds.): Artificial Intelligenceand Soft Computing : 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019 : proceedings. Pt. 1, Lecture Notes in Artificial Intelligence, no. 11508, 2019, Cham, Springer, Springer, ISBN 978-3-030-20912-4 (eBook)
- Keywords in English
- induction motor, malfunction detection, classification, pattern recognition, computational intelligence methods
- DOI
- DOI:10.1007/978-3-030-20912-4_61 Opening in a new tab
- URL
- https://link.springer.com/chapter/10.1007/978-3-030-20912-4_61 Opening in a new tab
- Related project
- Modele i algorytmy dla potrzeby sterowania i diagnostyki układów przetwarzania energii. . Project leader at PK: , ,
Działalność statutowa - Language
- eng (en) English
- Score (nominal)
- 20
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus, CORE
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTe991283a4a3f4656886805df3472bbc7/
- URN
urn:pkr-prod:CUTe991283a4a3f4656886805df3472bbc7
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.