Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

Purpose. Analysis of the rotor configuration of line start permanent magnet synchronous motor (LSPMSM) when using 3-bar magnet structure with two cases: one with a separating magnetic steel bridge and one without it. Research results allow selecting the appropriate rotor configuration to obtain the best starting characteristics, current, torque, and performance. Thereby, it is possible to replace high efficiency LSPMSM with low efficiency induction motors used in ventilation and water pumping loads to improve the efficiency of electricity use in mining. Methodology. The article uses analytical methods and simulation methods on Ansys/Maxwell software and conducts a laboratory test evaluation to determine the effect of rotor configuration on the starting characteristics, working current, torque ripple, and working efficiency of an LSPMSM for mining applications. Findings. An LSPMSM model was built based on a 15 kW–3,000 rpm induction motor with two rotor configurations: one with a separating magnetic steel bridge and one without it. The results of the study show that when a rotor configuration has no separating magnetic steel bridge, the LSPMSM reaches its maximum speed in 0.45 seconds, a synchronous speed of 3,000 rpm in around 0.75 seconds, a torque ripple of 23.1 %, a current total harmonic distortion of 14.3 %, and a performance of 93.3 %. In contrast, when a rotor structure has a separating magnetic steel bridge, starting the motor is more difficult, taking 1.14 seconds to reach synchronous speed and having a lower starting torque, a current total harmonic distortion of 16.1 %, and a performance of 92.5 %. Originality. Research rotor configurations of LSPMSM based on the 15 kW–3,000 rpm induction motor with and without a separating magnetic steel bridge. The research results allow choosing the appropriate rotor configuration to obtain the best starting characteristics, current, operating torque and performance efficiency. Practical value. The research results are important scientific guidance in the design and manufacture of LSPMSM for application in underground mining to improve the efficiency of electricity use in mining.

Line Start Permanent Magnet Synchronous Motor (LSPMSM) has been proved as an economically feasible, high efficiency alternative to an induction motor where constant speed is desirable. These motors are having the properties of an induction motor during starting and synchronous motor during steady state period. The need of line starting, calls for a cage winding in addition to permanent magnets in the rotor, which increases the complications in rotor design, and restricts the choice of rotor geometries. There is always conflict in the rotor design of this machine. This conflict is in between synchronous and asynchronous performance of the machine which in turn is a conflict between cage and magnet placement in the rotor. This paper reports an attempt in the direction of removing this conflict and optimizes both synchronous and asynchronous performances of the machine. A new two-part rotor geometry is suggested and analyzed using the dynamic model of LSPMSM prepared in Simulink.

Starting phase of a motor in general is a very complicated period. Motors draw a large amount of current, several times the normal full-load current when first energized. In starting period of Line Start Permanent Magnet Synchronous Motors (LSPMSM), high current causes saturation in stator and rotor. The effect of saturation on the “q-axis” synchronous inductance, L mq , of LSPMSM is studied in this paper. The paper will proposes a method to calculate the L mq using the Lumped Parameter Method. Comparative results will be carried on using finite element method. Good agreement between results is justified.

Line start permanent magnet synchronous motors (LS-PMSM) are energy-efficient synchronous motors that can start asynchronously due to a squirrel cage in the rotor. The drawback, however, with this motor type is the chance of failure to synchronize after start-up. To identify the problem, and the stable operation limits, the synchronization at various parameter combinations is investigated. For accurate knowledge of the operation limits to assure synchronization with the utility grid, an accurate classification of parameter combinations is needed. As for this, many simulations have to be executed, a rapid evaluation method is indispensable. To simulate the dynamic behavior in the time domain, several modeling methods exist. In this paper, a discussion is held with respect to different modeling methods. In order to include spatial factors and magnetic nonlinearities, on the one hand, and to restrict the computation time on the other hand, a magnetic equivalent circuit (MEC) modeling method is developed. In order to accelerate numerical convergence, a mesh-based analysis method is applied. The novelty in this paper is the implementation of support vector machine (SVM) to classify the results of simulations at various parameter combinations into successful or unsuccessful synchronization, in order to define the synchronization capability limits. It is explained how these techniques can benefit the simulation time and the evaluation process. The results of the MEC modeling correspond to those obtained with finite element analysis (FEA), despite the reduced computation time. In addition, simulation results obtained with MEC modeling are experimentally validated.

The paper presents a 2D field-circuit model of coupled electromagnetic thermal and mechanical phenomena in a line start permanent magnet synchronous motor (LSPMSM). The nonlinearity of the magnetic circuit, the influence of temperature on magnetic properties of the permanent magnets as well as on electric and thermal properties of the materials have been taken into account. The elaborated on the basis of this model algorithm as well as a developed computer program have been used to analyze the influence of temperature and armature reaction on the magnet stabilization during the starting process of the considered motor. The selected results of simulations have been presented and discussed.

The variation of stator winding turns will directly affect the key parameters of a motor, such as winding resistance and winding reactance, which further affect the steady-state performance of the motor. In order to get excellent steady-state performance from line-start permanent magnet synchronous motors (LSPMSMs) under different load powers, taking an 11 kW LSPMSM as an example, the finite element method (FEM), combined with the steady-state phasor diagram and torque angle characteristic, are used in this paper for the optimal design of the stator winding turns of the prototype. The correctness of the model is verified by comparing the experimental data with the calculated data. First, the influences of different stator winding turns on the no-load, back-induced electromotive force (EMF), as well as on inductance and overload ability are studied, and the variation mechanism is obtained. In addition, from the perspective of the torque angle characteristic, the influence of the change in synchronous inductance caused by the number of turns on the steady-state power angle is studied. Second, the variation of the current and power factors with turn number is obtained by studying the steady-state power angle and end voltage. Based on the coupling relationship between the no-load back EMF and the power angle, the mechanism of non-linear variation of current and power factor is revealed. Finally, the variation of the number of turns on the core loss and eddy current loss is analyzed under various operating conditions, and the variation mechanism is revealed, based on the armature reaction theory.

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%.

In the design process of the existing Line-Start Permanent Magnet Synchronous Motor (LSPMSM), the method to achieve the optimum performance is complicated, and the problem of pull-in synchronization caused by large load inertia is not fully considered. The objective of this research is to introduce a multi-objective optimization technique with efficiency (Eff), power factor (PF), pull-in torque multiple and starting torque multiple as optimization objectives.The impact of parameters such as core length, air gap length, and magnet thickness on efficiency, power factor, pull-in torque, and starting torque are analyzed. To achieve the optimal design outcome, a surrogate model integrating Response Surface Methodology (RSM) and Finite Element Analysis (FEA) for LSPMSM has been developed.The initial scheme based on FEA is analyzed and compared with the optimal scheme in detail, and the prototype manufactured according to the optimal scheme is tested and verified. The results of the optimization analysis show that in the case of a large load inertia, the efficiency, power factor, starting torque multiple and pulse-in torque multiple of the new optimization scheme have all been improved, achieving the collaborative optimization of transient and steady-state performance. Moreover, the results derived from FEA exhibit a high degree of consistency with the experimental data, thereby validating the optimized motor’s superior performance in both transient response and steady-state operation.

The Line-Start Permanent Magnet Synchronous Motor (LSPMSM) attracted a considerable attention because of the higher value of the product between the power factor and the efficiency. The paper proposes an analytical design method for the LSPMSM, considering the asynchronous starting and the synchronous steady state parameters. Using this theoretical approach, all the synchronous and asynchronous starting characteristics will be calculated. The design of a motor having the following specifications: rated output power 3.5 kW, rated voltage 200 V, rated frequency 50 Hz, rated speed 3000 rpm is presented.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

This article presents a design improvement of the dual‐pole, two‐speed line start permanent magnet (LSPM) synchronous motor. For this purpose, the operational performance of the motor with dual‐pole structure is first compared with the conventional single‐pole motor to identify its performance limitations. Next, the structure of the dual‐pole rotor is modified accordingly to address the related limitations. The proposed, modified design reduces the number of PMs in the rotor structure and offers a better steady state and transient performance. The motor is analysed using 2D finite element analysis (FEA), and results are verified by experimental tests on the motor prototype.

Traditional fast Fourier transform (FFT) has gained enormous recognition for broken rotor bar (BRB) fault detection in induction motors using the sideband features as fault indices, however, the false alarm from inaccurate diagnosis remains a major setback associated with the technique. This paper presents two reliable spectral analysis approaches for BRB fault detection and analysis for induction motors: Thompson Multitaper (MTM) power spectral density (PSD) estimate, and Welch PSD estimate. The two methods are implemented using the simulated stator current signal of an induction motor obtained by the finite element method. The Finite Element analysis software, ANSYS, is used to design and simulate different motor conditions: a healthy motor, a motor with one, two and three BRBs. It is verified that the proposed methods provide robust and reliable BRB fault detection for induction motors.

In this paper, the performance of machine learning methods for squirrel cage induction motor broken rotor bar (BRB) fault detection is evaluated. Decision tree classification (DTC), artificial neural network (ANN), and deep learning (DL) methods are developed, applied, and studied to compare their performance in detecting broken rotor bar faults in squirrel cage induction motors. The training data were collected through experimental measurements. The BRB fault features were extracted from measured line-current signatures through a transformation from the time domain to the frequency domain using discrete Fourier Transform (DFT) of the frequency spectrum of the current signal. Eighty percent of the data were used for training the models, and twenty percent were used for testing. A confusion matrix was used to validate the models’ performance using accuracy, precision, recall, and f1-scores. The results evidence that the DTC is less load-dependent, and it has better accuracy and precision for both unloaded and loaded squirrel cage induction motors when compared with the DL and ANN methods. The DTC method achieved higher accuracy in the detection of the magnitudes of the twice-frequency sideband components induced in stator currents by BRB faults when compared with the DL and ANN methods. Although the detection accuracy and precision are higher for the loaded motor than the unloaded motor, the DTC method managed to also exhibit a high accuracy for the unloaded current when compared with the DL and ANN methods. The DTC is, therefore, a suitable candidate to detect broken rotor bar faults on trained data for lightly or thoroughly loaded squirrel cage induction motors using the characteristics of the measured line-current signature.

A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar (BRB) fault diagnosis of induction motors. Discrete Fourier transform (DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence effect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm (called TR-MBPSO) based on a modified bare-bones particle swarm optimization (BPSO) and trust region (TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10−4, but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.

A line-start permanent magnet synchronous motor (LSPMSM) is designed based on modifying the rotor of a 4kW commercial induction motor (IM). The designed LSPMSM provides a higher output power, efficiency and power factor as well as lower maximum operating temperature than the IM. Finite-element electromagnetic and thermal analyses are used to compare thermal behavior of the motors and verify the accuracy of the assumed temperature rise.