Detection of the Interturn Shorts of a Three-Phase Motor Using Artificial Intelligence Processing Vibration Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F22%3APU147398" target="_blank" >RIV/00216305:26620/22:PU147398 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9982824" target="_blank" >https://ieeexplore.ieee.org/document/9982824</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ME54704.2022.9982824" target="_blank" >10.1109/ME54704.2022.9982824</a>
Alternative languages
Result language
angličtina
Original language name
Detection of the Interturn Shorts of a Three-Phase Motor Using Artificial Intelligence Processing Vibration Data
Original language description
The paper deals with description, design, learning and inference process of a convolutional 2D neural network for detection of shortened winding turns of a three-phase permanent magnet synchronous motor. Input datasets for aforementioned procedures have been created by sensing vibration data on the real motor using accelerometers with a possibility of artificially induce short circuit in the motor winding. Only simple pre-processing of a time signal has been done – the time waveform was reshaped into 2D greyscale images with a size of 64 x 64 points and led directly into the neural network. No pretrained network has been used – internal parameters have been learned from scratch. Learning process as well as inference of the network have been performed on a standard personal computer with nVidia GeForce RTX 2080 Ti graphics card and implemented usign Python in Keras/TensorFlow environment. Datasets for different working states of the motor, such as speed, torque, error type and its severity have been used. Training procedure of the network has been done within lower tens of minutes and final validation accuracy was 100 % in the most cases, while classification accuracy during inference process has reached the value of more than 99 %. Obtained results confirmed a fact, that faults’ detection of the mechatronic system based on sensing of mechanical quantities and their evaluation is very reliable even in the case of electrical-based faults.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
<a href="/en/project/8A19001" target="_blank" >8A19001: Artificial Intelligence for Digitizing Industry</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2022 20th International Conference on Mechatronics - Mechatronika (ME)
ISBN
978-1-6654-1040-3
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
234-238
Publisher name
IEEE
Place of publication
neuveden
Event location
Pilsen
Event date
Dec 7, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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