Convolutional Neural Network-Based Online Stator Inter-Turn Faults Detection for Line-Connected Induction Motors
- Resource Type
- Periodical
- Authors
- Nazemi, M.; Liang, X.; Haghjoo, F.
- Source
- IEEE Transactions on Industry Applications IEEE Trans. on Ind. Applicat. Industry Applications, IEEE Transactions on. 60(3):4693-4707 Jun, 2024
- Subject
- Power, Energy and Industry Applications
Signal Processing and Analysis
Fields, Waves and Electromagnetics
Components, Circuits, Devices and Systems
Induction motors
Stators
Fault detection
Convolutional neural networks
Permanent magnet motors
Harmonic analysis
Stator windings
Convolutional neural network
fault detection
fault severity
fundamental frequency phasor magnitude
induction motor
stator inter-turn fault
third harmonics
- Language
- ISSN
- 0093-9994
1939-9367
Stator inter-turn faults (SITFs) constitute a significant portion of induction motor failures. In this paper, a novel two-dimensional (2D) convolutional neural network (CNN)-based SITFs detection technique is proposed for line-connected induction motors, where the fundamental frequency phasor magnitude (FPM) and the 3rd harmonic components of the stator currents are used as signals. The proposed technique extracts FPMs from the measured three-phase stator currents of an induction motor through the digital Fourier filtering and then reconfigures them into three-dimensional (3D) images using the current to image transformation (CIT) mechanism to form image datasets. The same preprocessing procedure is applied to the 3rd harmonic components. The proposed approach is validated using experimental data measured in the lab for a 2.2 kW induction motor under various healthy and SITFs conditions, along with 12 motor loadings and an unbalanced voltage supply. It shows robust SITFs detection performance under load variations and power supply asymmetry. FPMs and SITFs work equally well for the SITFs detection and fault severity assessment; while the faulty phase can be effectively identified by FPMs.