Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using th...

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Bibliographic Details
Published in:Energies (Basel) 2020-03, Vol.13 (6), p.1475
Main Authors: Skowron, Maciej, Orlowska-Kowalska, Teresa, Wolkiewicz, Marcin, Kowalski, Czeslaw T.
Format: Article
Language:English
Subjects:
Analytical methods
Artificial intelligence
Circuits
Classification
convolutional neural network
Deep learning
Diagnostic systems
Efficiency
Fault detection
Fault diagnosis
induction motor drive
Induction motors
Information processing
inter-turn short circuits
Methods
Motor stators
Neural networks
Short circuits
Signal processing
Wavelet transforms
Winding
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Summary:In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13061475