Rotor Bars Research Articles

Improved Techniques for Analyzing Broken Rotor Bars at Different Supply Frequencies in Inverter-Fed Induction Machines

Improved Techniques for Analyzing Broken Rotor Bars at Different Supply Frequencies in Inverter-Fed Induction Machines

The Analysis of Odd Triple Harmonics to Detect Broken Rotor Bar in Delta-Connected Induction Motors

The Analysis of Odd Triple Harmonics to Detect Broken Rotor Bar in Delta-Connected Induction Motors

О возможности проведения контроля состояния асинхронных электродвигателей с частотно-регулируемым приводом методом спектрального анализа

One of the important tasks of current energy power industry is to increase the reliability of induction motors operation. A promising direction for monitoring their technical condition is application of methods of spectral analysis of signals. However, very little attention is paid to the diagnostic assessment of induction motors as a part of a variable frequency drive, as well as the use of frequency converters for diagnostic purposes. Therefore, the issues related to monitoring the condition of induction motors with variable frequency drives are relevant and require scientific research. The purpose of the study is experimental evaluation of the possibility to use the spectral analysis method to assess the technical condition of induction motors driven by frequency converters. The research has been carried out on AIR71A6 induction motor with a serviceable rotor winding and one broken rotor bar rotor winding. The internal magnetic field is considered as a diagnostic signal in steady-state and start-up mode. The condition has been evaluated based on the determination of harmonics in the amplitude spectrum from the dummy rotor winding. A Short Time Fourier Transform is used for signal processing. The spectra of the internal magnetic field have been obtained at start-up and in steady-state mode when the engine is fed by an autotransformer and frequency converter. A diagnostic sign of the presence of broken bars, that is an increase of harmonic amplitudes from the dummy rotor winding, manifested itself not only when the electric motor is fed by an autotransformer, but also frequency driven. Comparing the amplitudes of harmonics in the presence and absence of a frequency converter allows us to conclude that interference of the converter slightly affect the amplitudes of specific harmonics. Using the example of an internal magnetic field signal, it is shown that spectral analysis methods can be used to diagnose the induction motors condition with a variable speed drive.

Detection of Broken Bars in Induction Motors Operating with Closed-Loop Speed Control

Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate the speed. In this paper, the potential of zero-sequence voltage analysis to detect this fault is investigated, and a new index to quantify the severity of the fault based on this signal is proposed. Signals from motors operating under different control strategies and signals from motors powered from the mains are considered to verify the robustness of the proposed fault severity index. As a result, in all the analysed conditions the value of the proposed index for the healthy motor is found to be approximately 0.010, while for the faulty machine it is between 0.110 and 0.252.

Analysis of Vibration Characteristics Considering the Modulation Effect According to Rotor Bars of Induction Motor for EVs

Analysis of Vibration Characteristics Considering the Modulation Effect According to Rotor Bars of Induction Motor for EVs

Relationship between the induced voltage of a rotor bar and the rotation characteristics of a high-temperature superconducting induction motor

Although high-temperature superconducting induction motors have a simple structure, they are distinguished by their high efficiency and high power density. However, an accurate theoretical model that defines the primary electrical characteristics controlling the motor has not been established because of the nonlinear characteristics of high-temperature superconducting squirrel-cage rotor winding. As part of establishing such a model, an experiment was conducted to analyze the relationship between the induced voltage of the rotor bar in a high-temperature superconducting squirrel-cage rotor winding and stator-side quantities (voltage and transport current), as well as its rotation characteristics. A subject was a 1.5 kW class induction motor using Bi–Sr–Ca–Cu–O high-temperature superconducting rotor bars and the experiment was performed at a temperature and a frequency of 77 K and 60 Hz, respectively. The induced voltages of the rotor bars were measured by applying a rotating magnetic field and exhibited a distorted waveform characterized by a third-order harmonic in a magnetic flux flow state. Despite the distorted voltage waveform of the rotor bar, the stator transport current responsible for driving the induction motor remained unaffected and the motor initiated rotation in a slip rotation mode. From the above results, it was experimentally clarified that a stator voltage exceeding the critical current of the rotor bar must be applied when starting the induction motor. It was also shown that the rotor bar can be regarded as a constant resistance for a constant effective value of the stator current when transitioning the motor into a slip rotation state.

Efficiency-Centered Fault Diagnosis of In-Service Induction Motors for Digital Twin Applications: A Case Study on Broken Rotor Bars

The uninterrupted operation of induction motors is crucial for industries, ensuring reliability and continuous functionality. To achieve this, we propose an innovative approach that utilizes an efficiency model-based digital shadow system for in situ failure detection and diagnosis (FDD) in induction motors (IMs). The shadow model accurately estimates IM losses and efficiency across various operational conditions. Our proposed method utilizes efficiency as the primary indicator for fault detection, while losses serve as condition indicators for fault diagnosis based on real-time motor parameters and loss sources. We introduce a bond graph as a fault diagnosis network, linking loss sources, motor parameters, and faults. This interconnected approach is the key aspect of our proposed diagnostic method and aims to be used in fault diagnosis as a general method. A case study of a broken rotor bar is used to validate the proposed method using a dataset of five motors. Among these, one motor operates without failure, while the remaining four exhibit broken rotor faults categorized as 1, 2, 3, and 4. The proposed method achieves 99.99% precision in identifying one to four defective rotor bars in IMs. Comparative analysis demonstrates good performance compared to vibration-based FDD approaches. Moreover, our methodology is computationally efficient and aligned with Industry 4.0 requirements.

Метод обработки сигналов токов статора асинхронного двигателя для диагностики обрыва стержня ротора

The study is relevant due to high requirements to the reliability of the operation of auxiliary mechanisms of thermal power plants (TPP). The main source of mechanical power of auxiliary mechanisms of TPPs is high-voltage induction motor with squirrel cage rotor (IMSC). One of the most difficult causes of emergency shutdown to detect early is a broken rotor bar. The breakage of IMSC rotor bar is of latent character. It can be for a long time without considerable influence on the machine operation mode. Nevertheless, the fact of the breakage can be considered as an emergency condition of IMSC, and the consequences of leaving the groove of the magnetic core are irreversible. Scientifically, early diagnostics of IMSC rotor bars breakage of TPP auxiliary needs is both a difficult and extremely urgent task. The existing methods of diagnostics of such damage are based on measurement and analysis of vibration and acoustic physical quantities. The most common electrical parameter, used as a source of information, as a rule, is current consumption. And most of methods are based on Fourier analysis. The purpose of the study is to develop a mathematical algorithm to process digitized curves of stator phase currents to diagnose high-voltage IMSC. The theory of electric machines, the method of analytical approximation of experimental data, the method of regression analysis have been used to solve the problem. Experimental data is obtained, and testing of the algorithm is conducted on an experimental installation with a 0,4 kV induction motor with a modified design of the active part of the rotor developed for physical simulation of IMSC broken rotor bar. This paper proposes a new method of digital processing of stator current signals based on the regression analysis method for diagnostics of IMSC rotor winding. The authors have developed an algorithm for mathematical processing of digitized stator current curves based on regression analysis in the cosine-sine basis to identify the diagnostic sign of failure of one rotor bar of IMSC rotor. The authors have determined the number of harmonic components that best describe the initial signal from the point of view of the balance between saving computational resources and accuracy of approximation. A mathematical algorithm to detect the rotor bar breakage of an induction motor based on regression analysis of stator currents has been obtained and tested. It allows to detect rotor circuit damage with 97 % accuracy. At the same time the diagnostic sign changes its level when one bar breaks by 5 %. It is recommended to adapt the algorithm to the programming languages of microprocessor relay protection units.

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

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.

A New Modeling Approach and Comprehensive Monitoring of Electrical Faults Through Spectral Analysis in DSIM

The objective of this study is to evaluate the dependability of double-star induction machines by employing meticulous monitoring and intelligent handling of potential electrical malfunctions that may arise during their functioning. Specifically, the research delves into the examination of three distinct fault types: rotor bar breakage, stator phase opening, and inter-turn short circuit. Utilizing a mathematical model developed in the natural reference frame (abc), our aim is to comprehensively delineate both normal and faulty operational scenarios. To categorize and gauge the gravity of identified faults, we employ a methodology rooted in spectral analysis. In order to ensure continuous machine operation, especially in instances of stator phase opening, our proposed mitigation approach entails intentionally opening a secondary phase positioned at a 90-degree angle to the faulty phase. This strategic alteration transforms the machine into a dual two-phase configuration. Through rigorous simulation analyses, our findings underscore the efficacy and pragmatic viability of the devised fault detection technique, offering valuable insights into the domain of electrical machine reliability and fault management.

An Advanced Diagnostic Approach for Broken Rotor Bar Detection and Classification in DTC Controlled Induction Motors by Leveraging Dynamic SHAP Interaction Feature Selection (DSHAP-IFS) GBDT Methodology

This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC’s closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions.

Selection of induction motor stator and rotor slot number combination for optimal NVH

With the rise of vehicle electrification, considerable research has been directed towards reducing motor noise, predominantly focusing on Permanent Magnet Synchronous Motors (PMSM). Despite the increasing popularity of the induction motor, driven by its reliability, cost-effectiveness, and lower environmental impact, discussions surrounding the Noise, Vibration, and Harshness (NVH) aspects of induction motors remain limited. This paper addresses the NVH implications associated with stator and rotor slot combinations in conventional squirrel-cage induction motors. Through a design parametric study that examines the impact of varying the number of rotor bars through analytical simulation, the findings not only identify the optimal slot combination for minimizing motor noise but also demonstrate the critical importance of selecting the proper slot configuration, as NVH performance can exhibit significant variations. A physical test was conducted to validate the analytical simulations. This research contributes valuable insights into the induction motor design, especially at the early design stage, emphasizing the necessity of considerations in achieving enhanced NVH performance.

HILBERT Transform as a Technique to Distinguish Between Broken Rotor Bar Fault and Load Torque Variation

HILBERT Transform as a Technique to Distinguish Between Broken Rotor Bar Fault and Load Torque Variation

FPGA real-time implementation of welch transform for diagnosis of broken rotor bars in induction motors

FPGA real-time implementation of welch transform for diagnosis of broken rotor bars in induction motors

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning.

This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.

Research on rotor fault diagnosis technology of three-phase asynchronous motor based on NA-MEMD mutual information and SVM

Aiming at the problem of mode aliasing in the adaptive decomposition of nonlinear and non-stationary current signals generated by three-phase asynchronous motor faults, and the fault features contained in signals collected by a single sensor can not be accurately and comprehensively extracted and characterized when early rotor bar breakage and air gap eccentricity faults occur, A fault diagnosis method for three-phase asynchronous motor based on noise assisted multivariate empirical mode decomposition (NA-MEMD) and mutual information is proposed. Firstly, the NA-MEMD algorithm is used to decompose the three-phase stator current signal of the asynchronous motor to obtain multi-scale intrinsic mode functions (IMFs). Then, the correlation algorithm is used to screen the IMFs containing useful information. Then, the filtered IMF components are reconstructed into new signals and their features are extracted, Finally, support vector machines (SVM) are used to identify the rotor broken bars and air gap eccentric faults of the three-phase asynchronous motor. The experimental results show that the NA-MEMD method has a higher recognition rate than the traditional empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) methods.

Predictive torque control of induction motor for rotor bar faults diagnosis

Unlike DC motors and synchronous motors, they are maintenance-free motors due to the absence of brushes, commutators and slip rings. Induction motors can be operated in polluted and explosive environments as they do not have brushes which can cause sparks. In this paper, the performance of two control techniques, namely direct torque control (DTC) and predictive torque control (PTC), are compared in transient and static states when applied to a faulty induction machine (IM). The current and torque ripples is evaluated in a healthy machine, as well as in the presence of faults, at various speed and load values. During the transient state, the objective is to assess the method that provides the optimal dynamic response, which is achieving the desired speed without any overshooting while during the static state, the objective is to minimize torque ripple and harmonics in the stator current. The Discrete Wavelet Transform (DWT) is used to analyze stator phase current. In addition, the energy eigen value (EEV) analysis has been used to determine the fault severity. The healthy and faulty systems are simulated using Matlab/Simulink for the two control methods. The results show the superiority of the PTC method compared to the DTC. A comparison of the proposed control method with other works reported in the literature is performed to verify the superiority of the proposed strategy.

Fault Diagnosis of Induction Machine for Rotor Cage Damage Using MCSA for Industrial Application

The induction machine plays a vital role in industries. Induction machine failure causes significant process delays, higher maintenance expenses, and income loss. The machine’s predictive maintenance becomes crucial to find the primary cause of the defect. This paper focuses on the experimental results of induction machines used in the cement industry. Motor Current Signature Analysis is used to analyze the condition of rotor bars due to distorted input supply causing harmonics. The significance of this test is to determine the primary cause of the unexpected failure. The experimental results of the machine reveal distorted input with harmonics resulting in torque ripples. This might lead to broken rotor bars in the rotor cage with sidebands resulting in poor efficiency of the machine. Motor Current Signature Analysis predicts the lifespan of the machine to avoid unexpected failure. The Finite Element Method is used to design the machine for conditions one, two adjacent, and two non-adjacent broken rotor bars to observe the dynamics of rotor bar currents with numerical results. After a substantial overhaul of the machine, the proposed method is again performed, resulting in improved machine efficiency of 88.83%.

Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions

Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine. References 31, table 1, figures 9.

FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods.

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.