Rotor Bars Research Articles

Start-up transient analysis using CWT and ridges for broken rotor bar fault diagnosis

Start-up transient analysis using CWT and ridges for broken rotor bar fault diagnosis

Introduction of a modified thermal model for squirrel cage induction motor based on calculation of rotor bars' current

Introduction of a modified thermal model for squirrel cage induction motor based on calculation of rotor bars' current

Hybrid winding function method for inductance analysis of a line start synchronous reluctance machine

In recent years, there has been renewed interest in line-start synchronous reluctance machines (LSSRMs) due to their simple construction, magnet-free rotor, and low cost. To improve control performance, design optimization, and fault diagnostic analysis of these machines, it requires accurate estimation of their electromagnetic characteristics using detailed and time-consuming finite element analyses (FEAs). In this paper, inductances and electromagnetic torque of the LSSRM were calculated using the combination of winding function analysis and conformal mapping instead of FEA. The hybrid approach can be applied to the prediction of motor behavior, taking into account all space harmonics of the air-gap permeance without any restriction on the rotor saliencies and topologies. The influence of the core saturation, stator slots, and rotor bars were also considered. The results obtained by simulations were compared with FEA in terms of accuracy and computational time.

An Enhanced Pathfinder Algorithm based MCSA for rotor breakage detection of induction motor

This paper proposes a new broken rotor bar (BRB) finding technique based on a novel hybrid algorithm, namely the Enhanced Pathfinder Algorithm (EPFA). Compared to existing optimization algorithms, this proposed approach provides more balanced exploration and exploitation. The main task regarding rotor fault detection is to identify sideband frequency components (SFCs) corresponding to the fault and in this context proposed method overcomes the disadvantages of traditional frequency domain analysis, such as short data length, effects of spectral leakage, and insufficient sampling frequency. To achieve the proposed diagnosis approach, a signal model for faulty current and a function of residual error are used for the formulation of a nonlinear least-square problem. The method is first tested on a synthetic fault signal, and finally, to validate the proposed technique, a Finite Element Model (FEM) of the induction motor is simulated in the ANSYS 2D workbench.

Bond graph model of line-start permanent-magnet synchronous motors

The bond-graph (BG) enables to model multi-physics systems in the energy domains. This paper shows that BG models are an alternative, more intuitive and simpler to interpret, graphical tool as compared to existing dynamic models for performance prediction of the line-start permanent-magnet synchronous motors (LSPMSMs). It includes electrical, magnetic, and mechanical energy domains where the stator windings, rotor bars, and permanent magnets are the main components of the model. The dynamical equations of the proposed BG are extracted and solved using the MATLAB ordinary differential equation solver for a sample LSPMSM. The dynamics of the studied LSPMSM is investigated under two operating conditions (1) a healthy motor and (2) a motor with broken rotor bars. The simulation results are compared with the finite-element analysis (FEA) as the most accurate method to simulate the electric motor behaviour. It is shown that the proposed BG model accurately predicts the performance parameters of the motor such as current, torque, and speed under various operating conditions. In addition to the modelling validation, the transient behavior of the LSPMSM are discussed to describe the possible reasons for its behaviour during transient and steady-state operating conditions.

Experimental Diagnosis of Broken Rotor Bar Faults in Induction Motors at Low Slip via Hilbert Envelope and Optimized Subtractive Clustering Adaptive Neuro-Fuzzy Inference System

Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not provide satisfactory results if it is applied directly to the stator current signal at low slip because a high-resolution spectrum is required to separate the different components of the frequency. To address this problem, this paper proposes an efficient method based on a Hilbert fast Fourier transform (HFFT) approach, which is used to extract the envelope from the stator current using the Hilbert transform (HT) at low slip. Then, the stator current envelope is analyzed using the fast Fourier transform (FFT) to obtain the amplitude and frequency of the particular harmonic. These data were recently collected and selected as BRB fault features and were employed as adaptive neuro-fuzzy inference system (ANFIS) inputs for BRB fault autodiagnosis and classification. To identify the BRB defect by determining the number of broken bars in the rotor, two ANFIS models are proposed: ANFIS grid partitioning (ANFIS-GP) and ANFIS-subtractive clustering (ANFIS-SC). To validate the effectiveness of the proposed method, three different motors were used during experiments under various loads; the first was with one broken bar, the second was with two adjacent broken bars, and the third was a healthy motor. The obtained results confirmed the effectiveness and the robustness of the proposed method, which is based on the combination of HFFT-ANFIS-SC to diagnose the BRB faults and quantify the number of broken bars under different load conditions (under low and high slip) precisely with minimal errors (this method had an MSE of 10-14 and 10-7 for the RMSE) compared to the method based on the combination of HFFT-ANFIS-GP.

Optimized Thermal Modeling and Broken Rotor Bar Fault Detection of BDFIG with Discrete Wavelet Transform

Nowadays, energy demand is increasing with the developing technology and industry. Due to the harmful effects of fossil fuels, which have a significant share in energy production and the fact that these fossil fuels will soon be consumed, alternative energy sources are needed to meet the unlimited request and demands of people on energy. Considering the sustainability of the fuel used in the production of energy and environmental damage, the importance of renewable energy sources increases. With the help of developing technology and research studies, the existing generator types have been tried to be improved and new types have been proposed. One of the proposed and still developing generator types is the brushless dual-supply asynchronous generator. The rotor structure can be coiled or squirrel cage type. In this study, the designs of the mentioned generator which are made so far have been investigated. As well as the electromagnetic field solution of the generator was made and further developed. The optimized thermal modeling of the machine has been proposed. In addition, rotor failure effect on magnetic field and flux was done by using analytical and finite element methods. Finally, detection of failure was carried out by using a discrete wavelet transform.

Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications

The induction motor (IM) defect diagnosis has been an important field of research in recent years. The development in control circuits for IM has piqued the interest of industrialists and researchers. This paper presents a method for detecting and quantifying broken rotor bar (BRB) faults via wavelets and energy Eigen value (EEV) estimation in voltage/frequency control-fed IM. The fast Fourier transform (FFT) extracts the signal’s amplitude and frequency components, while the discrete wavelet transform (DWT) decomposes it. In this paper, the energy estimation for each level of breakdown and the method to overcome the diagnose faults are explained. The EEV of the motor current of the signal determines the fault’s severity and provides a better method for identifying the faults. The usage of a single current sensor is a gain of this technology. With a fluctuating load, we can identify the issue and the number of broken bars via online. After processing of DWT, the faulty BRB’s stator current signal is suppressed to 91% in amplitude when compared to existing techniques. Simulation and experimental results have proved that the proposed method’s stability, durability, and resilience.

Uncertainty Quantification of Input Parameters in a 2-D Finite-Element Model for Broken Rotor Bar in an Induction Machine

In this article, a forward uncertainty propagation method is presented for a 2-D finite-element (FE) model in an induction machine. This method is applied to quantify the uncertainty of input parameters, for example, dimensions and material properties, and demonstrate their variability effect on harmonics related to the broken rotor bar (BRB) faults. To show the most influential input parameters in the case of BRB harmonics, a global sensitivity analysis is performed from the polynomial chaos expansion (PCE) approximation of the FE model. The results of this study indicate that BRB harmonics are highly sensitive to stator inner diameter, rotor outer diameter, rotor bar conductivity, and core materials. Moreover, the combined variability of these sensitive input parameters can attenuate the amplitude of the BRB harmonics 30%–90% compared to the simulation results at nominal values of input parameters and closely match with measurement results.

Diagnosis of induction motor failures using discrete wavelet transform of an auxiliary winding voltage

Over last years, great attention has been developed to avoid the machines breakdown especially in the squirrel cage induction motor which suffer from different failures. The aim of this paper is to present a new method for the diagnosis of rotor bar breakage and end ring in induction machine fed by an inverter based on discrete wavelet transform applied on the voltage of an auxiliary winding as a new monitoring signature. The expression of the auxiliary winding voltage related to a small coil inserted between two stator phases is presented. The study is focused on the high-level signals of approximation and details coefficients. Thus, the evolution of any frequency of interest in the waveform is given in this paper. The method is validated by simulations of four broken bars and end ring cases under unloaded and loaded machine.

Fourier-Based Adaptive Signal Decomposition Method Applied to Fault Detection in Induction Motors

Time-frequency analysis is commonly used for fault detection in induction motors. A variety of signal decomposition techniques have been proposed in the literature, such as Wavelet transform, Empirical Mode Decomposition (EMD), Multiple Signal Classification (MUSIC), among others. They have been successfully used in many works related with the topic. Nevertheless, the studied signals present amplitude changes and chirp-type frequency components that are difficult to track and isolate with the aforementioned techniques. The contribution of this work is the introduction of a novel technique for time-frequency signal decomposition that is based on an adaptive band-pass filter and the Short Time Fourier Transform (STFT), namely Fourier-Based Adaptive Signal Decomposition (FBASD) method. This method is capable of tracking and extracting nonstationary time-frequency components within a user-selected frequency band. With these components, a methodology for detecting and classifying broken rotor bars in induction motors using the startup transient current is also proposed.

Learning Machine Based on Optimized Dimensionality Reduction Algorithm for Fault Diagnosis of Rotor Broken Bars in Induction Machine

Induction machine health monitoring is considered a developing technology for the online detection of faults that occur even at the initial stage. The objective of this study is to present an artificial intelligence (AI) technique for the detection and localization of adjacent and distant broken bar faults in the induction machine, through a multi-winding model for the simulation of these cases. In this work, it was found that the application of Artificial Neural Networks (ANN) based on Mean Squared Error (MSE) and Random Forest (decision tree) plays an important role in detecting and locating defaults. The stator current signal Ias of a motor in the dynamic state was acquired from a healthy and faulty motor with a broken rotor bar fault. 9 statistical features and 8 wavelet packet parameters are extracted from the stator current signal. These features were employed as an input vector to train and test the ANN and random fores29t and determine whether the motor was running under normal conditions or defective. For optimizing the rotor bar defect classification procedure, feature selection algorithms are adopted, such as BBAT and BPSO. For feature reduction, we used the principal component analysis (PCA) algorithm, to reduce the number of features. The results showed that the random forest classifier based on statistical parameters and wavelet packet parameters followed by PCA can detect the defective with high accuracy (98.3333%) compared to other methods.

Improved Torque and Efficiency of Induction Motors by Changing Rotor Structure of Permanent Magnet Assistance Synchronous Reluctance Motors

Many authors have recently studied line start permanent magnet assistance synchronous reluctance. This paper presents a method to improve the electromagnetic torque and efficiency of induction motors of 7.5kW-4P (which consists of 36 stator slots and 40 rotor bars) by changing the design of the permanent magnet assistance synchronous reluctance rotors. This means that permanent magnets will be inserted to the squirrel cage induction motors (induction motors). The electromagnetic torque and efficiency of an induction motor is analyzed and compared with that of the line-start synchronous reluctance motor via a finite element analysis. In addition, the influence of position and length permanent magnets on the electromagnetic torque and efficiency of the line-start synchronous reluctance motor is also considered and simulated. The model of this motor Linh is finally designed with four U layered- magnet rotor to verify the developed method. The development of the method is also validated on the practical induction motor.

A Study on the Effect of Rotor Bars of Induction Motor for Electric Vehicle

A Study on the Effect of Rotor Bars of Induction Motor for Electric Vehicle

A Novel Method for Diagnosis of Broken Bars Based on Optimum Resolution of Prescient Direction Algorithm

Fourier transformation (FT) and Multiple Signal Classification (MUSIC) method suffer the insufficient ability in diagnosing broken rotor bar (BRB) fault using short-time data. Theoretical and simulation analyses show that the Optimum Resolution of Prescient Direction (ORPD) algorithm has the best frequency resolution performance due to a priori knowledge. The main objective of this paper is to detect BRB faults in induction machines using a condition monitoring architecture based on ORPD algorithm. In the proposed application, the ORPD algorithm with the best frequency resolution performance is used to estimate the fault-sensitive frequencies in the stator current signature. The prior information of BRB fault characteristic distribution is used to construct a weighting matrix in the ORPD algorithm, for acquiring the lowest signal-to-noise ratio resolution threshold. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. The proposed methods were tested using experimental induction motors with different fault severity under the effect of several load levels or supply frequencies. Two types of power supply modes are considered: main and inverter. The results show that compared with traditional method which uses FT and MUSIC algorithm for fault diagnosing, the method based on ORPD algorithm has a higher frequency resolution and identification ability with short-time data, and still has good diagnostic performance even under light loading and lower supply frequencies.

Using principal component analysis and binary classification to detect static eccentricity faults in induction motors

Static eccentric faults can cause serious damage to induction motors if left undetected. Standard methods identify static eccentric faults by performing frequency or harmonic analysis of the motor currents and by monitoring for the principal slot harmonics. However, the principal slot harmonics arise only if the rotor bars and the pole-pair number of the motor conform to certain relationship. To overcome this limitation, this paper describes two machine learning methods to detect static eccentric faults using either voltage and/or current data. Principal Component Analysis, and a Binary Linear Classifier are used independently, to classify experimental data from both a healthy, and faulty motor. Both machine learning algorithms were written in MATLAB and their performance was evaluated using a standard confusion matrix.

Prediction of broken rotor bar in induction motor using spectral entropy features and TLBO optimized SVM

The information of the fault frequency characteristics is of great importance for all associated fault diag nostics. This requires a high-resolution spectrum analysis to achieve efficient monitoring of machinery faults, especially while diagnosing rotor bar breakage under light load conditions, because the fault frequencies almost overlap with the fundamental. In this context, rather than looking for frequencies associated with rotor faults, several frequency bands are observed separately in terms of the entropy contained within these bands. First, the motor current signal has been divided into several frequency bands using the continuous wavelet transform (CWT), and the spectral entropy is calculated from each band as the features to describe the rotor condition. Principal component analysis (PCA) has been used as a feature reduction tool, and the features projected onto the first two principal components have been fed into the SVM for inference. SVM is a supervised learning method used for classification and regression analysis. To improve classification performance, a radial basis function (RBF) kernel has been employed, and to find the optimal value of the kernel parameters, a metaheuristic approach, namely teaching learning-based optimization (TLBO), is utilized. The ANSYS 2D workbench is used to simulate the finite element model (FEM) of an induction motor with broken rotor bars, and the efficacy of the proposed method is then tested using simulated data. To investigate the robustness of the proposed approach, white Gaussian noise has also been added to the simulated data, and the performance of the SVM is tested with these spectral features.

Induction Motor Speed Estimation Based on Airgap Flux Measurement Using Hilbert Transform and Fast Fourier Transform

This paper proposes the rotor speed estimation of a squirrel cage induction motor fed by a sinusoidal power supply or an inverter based on the use of a Hall effect sensor installed between two stator slots of the machine. As well known in literature, inverter-fed induction motors usually contains a series of current harmonics and in case of rotor faults, such as broken bars, this operational condition becomes even more severe. The speed estimation has been extensively investigated for its use in control schemes, to compute output power or machine efficiency and particularly for fault diagnosis using motor current signature analysis. In this method, it is necessary an accurate speed estimation to prevent false positives and false negative indications. Therefore, the present research has been carried out by applying the Fast Fourier Transform and Hilbert Transform in the airgap signal measured by the Hall sensor, in order to estimate the rotational speed of the induction motor in a healthy condition and with broken rotor bars. This approach has been compared to the same signal processing techniques using the stator current phase, usually used for speed estimation in the state of the art solutions. The efficiency of this approach was evaluated from simulation and experimental tests, for motor running at distinct load torques, at very low slip conditions and considering a damaged rotor cage. The simulation and experimental results have shown a better accuracy with the measured or simulated speeds, using the airgap estimations when compared to those obtained from stator current processing.

DC Motor Control Technology Based on Multisensor Information Fusion.

To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has excellent speed regulation performance and is easy to control. It is widely used in applications that require high motor startup and speed regulation characteristics. This research mainly discusses DC motor control technology. Evidence theory can combine various fault information at different levels to enhance mutual support between pieces of evidence, thereby improving the accuracy of motor fault detection. Based on the steps of signal processing, feature extraction, feature dimensionality reduction, and state recognition, the research on the state recognition method of belt conveyor drive motor based on multisource information fusion is carried out. By studying the multisource information fusion, this paper proposes a two-stage belt conveyor drive motor information fusion model based on the optimal D-S evidence theory. The correct identification rate of broken rotor bars during fault monitoring is 99.8%. This method divides the specific motor fault feature set into multiple fault subspaces and uses different diagnostic neural networks and different fault feature parameters for local diagnosis, respectively. The scheme designed in this study significantly improves the recognition accuracy of the motor in the same working condition and under variable working conditions. The drive motor state recognition and intelligent decision-making system designed by combining the results of multisource information fusion can effectively describe the fault type and has strong operability.

Investigation of Starting Characteristics with respect to Starting-Bar Shape in a Double-Cage Induction Motor

This paper presents the new shape of a rotor bar improving the starting performances in a double-cage induction motor (IM). The rotor bar of a doble-cage IM is composed of a working bar, neck, and starting bar and each part has different effects on the characteristics of a motor. In this paper, it is confirmed that a starting bar has a dominant effect on the starting performances. The shape of a starting bar is designed by using the two ratios of the depth to width and the lower to upper width, respectively. The starting performances are closely analyzed by estimating the reactance and resistance of a rotor bar with respect to the depth through multi-layer method and FEA. Finally, by applying both the ratios to the starting bar, their mutual influence is scrutinized by using a response surface methodology and three models satisfying both starting torque and current aimed in this paper is proposed.