prindi

An advanced diagnostic approach for broken rotor bar detection and classification in DTC controlled induction motors by leveraging dynamic SHAP interaction feature selection (DSHAP-IFS) GBDT methodology

autor
Khan, Muhammad Amir
Asad, Bilal
Vaimann, Toomas
Kallaste, Ants
vastutusandmed
Muhammad Amir Khan, Bilal Asad, Toomas Vaimann, Ants Kallaste
allikas
Machines
kirjastus/väljaandja
MDPI
ajakirja aastakäik number kuu
vol. 12, 7
ilmumisaasta
2024
leheküljed
art. 495
leitav
https://doi.org/10.3390/machines12070495
märksõna
asünkroonmootorid
tehisintellekt
rikked
töökindlus
tehisõpe
ajamid
võtmesõna
broken rotor bars
artificial intelligence (AI)
fault classification
feature extraction
induction motors
condition monitoring
machine learning (ML)
frequency domain analysis
supervised learning
gradient-boosting trees
variable speed drives
time domain analysis
ISSN
2075-1702
märkused
Special Issue: Machine Learning Based Predictive Maintenance and Condition Monitoring
Bibliogr.: 53 ref
Open Access
oa
teaduspublikatsioon
teaduspublikatsioon
klassifikaator
1.1
Scopus
scopus
TTÜ struktuuriüksus
Elektroenergeetika ja mehhatroonika instituut
keel
inglise
Khan, M. A., Asad, B., Vaimann, T., Kallaste, A. An advanced diagnostic approach for broken rotor bar detection and classification in DTC controlled induction motors by leveraging dynamic SHAP interaction feature selection (DSHAP-IFS) GBDT methodology // Machines (2024) vol. 12, 7, art. 495. https://doi.org/10.3390/machines12070495