Zakariya Al-Hamouz

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.

The application of line-start permanent magnet synchronous motors (LSPMSMs) is rapidly spreading due to their advantages of high efficiency, high operational power factor, being self-starting, rendering them as highly needed in many applications in recent years. Although there have been standard methods for the identification of parameters of synchronous and induction machines, most of them do not apply to LSPMSMs. This paper presents a study and analysis of different parameter identification methods for interior mount LSPMSM. Experimental tests have been performed in the laboratory on a 1-hp interior mount LSPMSM. The measurements have been validated by investigating the performance of the machine under different operating conditions using a developed qd0 mathematical model and an experimental setup. The dynamic and steady-state performance analyses have been performed using the determined parameters. It is found that the experimental results are close to the mathematical model results, confirming the accuracy of the studied test methods. Therefore, the output of this study will help in selecting the proper test method for LSPMSM.

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.

Early detection of different faults will assist motor operation and prevent it from complete damage. Condition monitoring is extremely important to monitor the motor status and isolate it under failure conditions. This paper will propose a MATLAB® mathematical based neural network model for early detection of static eccentricity fault in line start permanent magnet synchronous (LSPMS) Motors. Under different combinations of fault-load conditions, the motor will be simulated to specify the characteristic of this fault. The line current will be utilized to extract the distinct principal components. The efficient selected components will be used as the input of neural network to recognize the percentage of occurrence as well as the fault's severity. The network will be trained over a specified range of static eccentricity degrees. Besides, it will be tested for unseen cases to qualify the effectiveness of the trained neural network. The testing results show a detection accuracy in a range between 95-98%.

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%.

Line start permanent magnet synchronous motors experience different types of failures, including static eccentricity. The first step in detecting such failures is the mathematical modeling of the motor under healthy and failed conditions. In this paper, an attempt to develop an accurate mathematical model for this motor under static eccentricity is presented. The model is based on the modified winding function method and coupled magnetic circuits approach. The model parameters are calculated directly from the motor winding layout and its geometry. Static eccentricity effects are considered in the motor inductances calculation. The performance of the line start permanent magnet synchronous motor using the developed mathematical model is investigated using MATLAB/SIMULINK^® software (2013b, MathWorks, Natick, MA, USA) under healthy and static eccentricity condition for different loading values. A finite element method analysis is conducted to verify the mathematical model results, using the commercial JMAG^® software (16.0.02n, JSOL Corporation, Tokyo, Japan). The results show a fine agreement between JMAG^® and the developed mathematical model simulation results.