Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase induction motors. A MATLAB-based model was developed and simulated under different fault-load combination cases for different sizes of motors. The motor’s developed electromechanical torque was selected as a fault indicator. For the design and training of the neural network, the mean, variance, max, min, and F120 time based on statistical and frequency-related features were found to be very distinct for correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In the training phase of the neural network, five different motors were used and are referred to as seen motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the electromechanical torque under different fault-load combination cases, previously never seen from the first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed accuracy in the range of 88–99%.

Accurate modeling is the first step in developing a model-based diagnostics tool for different types of failures in electric motors. In this paper, a mathematical model for an interior-mount line start permanent magnet synchronous motor (LSPMSM) under the stator inter-turn fault is developed based on the principles of magnetic coupled circuit theory, electromechanical energy conversion and reference frame theory. The developed mathematical model is simulated using MATLAB and experimentally validated using a 1-hp, 4-pole, 400-V LSPMSM. To investigate the performance of the motor under the stator inter-turn fault at different loading levels, experimental and simulation results are compared and analyzed for both transient and steady-state responses of the current and speed. The simulation and experimental results are in very good agreement and show that under the stator inter-turn fault, the current magnitude and time response of the phase that contains the inter-turn are highly affected. In addition, due to asymmetry in the stator windings under this fault, the speed experiences some oscillations at steady state. Hence, the developed model can be used to detect the possibility and location of the stator inter-turn fault in an LSPMSM.

This paper presents a novel stator inter-turn fault diagnosis method for Line Start Permanent Magnet Synchronous Motors (LSPMSMs) using the frequency analysis of acoustic signals resulting from asymmetrical faults. In this method, acoustic data are experimentally collected from a 1 hp interior mount LSPMSM for different inter-turn fault cases and motor loading levels, while including the background noise. The signals are collected using a smartphone at a sampling rate of 48,000 samples per second. The signal for each case is analyzed using fast Fourier transform (FFT), which results in the decomposition of the signal into its frequency components. The results indicate that, for both no-load and full-load conditions, 39 components are observed to be affected by the faults, whereby their amplitudes increase with the fault severity. The 40-turns fault shows the highest difference in the component amplitudes compared with the healthy condition acoustic signal. Therefore, this diagnostic method is able to detect the stator inter-turn fault for interior mount LSPMSMs. Moreover, the method is simple and cheap since it uses a readily available sensor.