Broken Rotor Bars Research Articles

Multi-Signal Induction Motor Broken Rotor Bar Detection Based on Merged Convolutional Neural Network

Motor fault detection plays a vital role in industrial maintenance. Timely detection of faults in their early stages can prevent catastrophic consequences and reduce maintenance costs. Traditional methods face challenges in motor broken rotor bar (BRB) detection: model-driven methods are difficult to apply accurately in complex and changing environments, while data-driven methods usually require sophisticated feature extraction and classification processes. In this paper, we propose a novel non-invasive fault detection method. The method preprocesses motor currents by Hilbert-Huang Transform (HHT) and Park’s Vector Modulus (PVM) and then uses a merged convolutional neural network (CNN) for classification. This experiment investigates the detection of broken rotor bars of motors with different loads (25%, 50%, 75%, and 100% of rated load) and different fault levels (Normal, 1BRB, 2BRB, 3BRB, and 4BRB). The results show that the model’s classification accuracy exceeds 95% under various operating conditions and can maintain high accuracy under low load conditions, thus addressing the limitations faced by existing methods. In addition, it is computationally efficient and can guarantee high real-time performance. This method combines advanced signal processing techniques and deep learning algorithms to provide a practical solution for motor broken rotor bar detection.

Fault diagnosis of induction motor rotor broken bars based on rectification technique and Goerztel algorithm

Abstract In the early diagnosis of broken rotor bars (BRBs) in induction motors (IMs), the fault sideband frequency under no-load or light-load conditions is weak and difficult to extract. Meanwhile, existing methods for obtaining speed information rely on speed sensors, which increases hardware costs. To address this issue, a speed estimation method has been adopted, and a novel diagnostic technique combination method has been investigated in this paper. This method utilizes the rectification technique as the demodulation technique, and the Goertzel algorithm is employed as the spectral analysis tool. The stator current is demodulated by the rectification technique, and the fundamental frequency component is converted into a DC component, which effectively suppresses the impact of the fundamental spectrum leakage on BRB fault diagnosis and speed estimation. To verify the investigated method, the experiments are carried out on two datasets obtained from the Sao Paulo University and the experimental platform, under different severity of BRBs and loads. The results show that the investigated method can obviously extract the fault characteristic frequency, and it is easy to implement with less computation, strong real-time performance, and low hardware cost.

Quaternion Signal Analysis for Detection of Broken Rotor Fault Degrees in Induction Motors

Fault detection in induction motors is essential for maintaining the reliability of industrial operations. In practical applications, induction motors experience gradual wear on critical components, such as rotor bars, affecting their performance. This paper introduces a new methodology for modeling predictive wear functions related to rotor faults in induction motors, providing accurate forecasts and optimal performance through Quaternion Signal Analysis in the time domain. Our approach accurately detects wear in broken rotor bars and anticipates their degradation over time. The methodology involves coupling four vibration signals from the motor, representing them as quaternion coefficients, and calculating their rotational attributes to derive a statistical mean. We employ polynomial and Fourier regression techniques to construct a predictive wear function. We assess its accuracy through root mean square error (RMSE) analysis, which improves with increased sample size and regression complexity. Our findings indicate that polynomial regression, particularly at the second degree, achieves superior RMSE results compared to Fourier regression, even within limited sample windows. This approach offers a robust framework for early fault detection and wear prediction in induction motors, supporting enhanced maintenance strategies.

Comparative Analysis of the Effect of Rotor Faults in the Performance of Low-Speed High-Torque Machines

Several studies have focused on modeling and analyzing the impact of rotor faults in conventional low-pole-count machines, while related research on low-speed high-torque (LSHT) machines with a high pole count remains limited. In these machines, the combination of low speed, high inertia, and high torque levels presents a critical application for advanced diagnosis techniques. The present paper aims to describe and quantify the impact of rotor faults on the performance of LSHT machine types during the design stage. Specifically, 10-pole and 16-pole synchronous reluctance machines (SynRMs), permanent magnet synchronous machines (PMSMs), and squirrel-cage induction machines (SCIMs) are assessed by means of detailed 2D simulations. The effects of eccentricity, broken rotor bars, and partial demagnetization are studied, with a focus on performance variations. The results show that LSHT PMSMs are not significantly affected by the partial demagnetization of a few magnets, and the same holds true for common faults in SynRMs and SCIMs. Nonetheless, a significant increase in torque ripple was observed for all evaluated faults, with different origins and diverse effects on the torque waveform, which could be hard or invasive to analyze. Furthermore, it was concluded that specialized diagnosis techniques are effectively required for detecting the usual faults in LSHT machines, as their effect on major performance indicators is mostly minimal.

Diagnosing Broken Rotor Bar Faults in Speed‐Sensorless Squirrel Cage Induction Motors Using Principal Component Analysis and Knowledge Graph

ABSTRACTSquirrel cage induction motors (SCIMs) are integral to numerous industrial applications, and the accurate monitoring and diagnosis of rotor bar conditions are paramount for enhancing system productivity and minimizing maintenance expenditures. However, the existing techniques for diagnosing broken rotor bars (BRBs) faults are often constrained by the voltage imbalance conditions and the low slip in practical applications. This paper introduces an innovative approach to BRBs fault diagnosis, using principal component analysis (PCA) and knowledge graph (KG) methodologies. PCA, which is robust to imbalanced conditions, is employed to demodulate the stator current signals and isolate the phase modulation (PM) component. The calculated PM index serves as a fault indicator. Concurrently, the KG framework is introduced to detect the BRB fault and quantify the severity. Experimental results demonstrate the effectiveness and reliability of the proposed method under different load levels and fault severities.

Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis.

Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF). The dataset was generated from three-phase signals of an induction motor controlled by a Direct Torque Controller under various operating conditions (20-1500 rpm with 0-100% load), resulting in 4251 images. The model, based on a Visual Geometry Group (VGG) architecture with 19 layers, achieved an overall accuracy of 98%, with specific accuracies of 99% for RAF, 100% for BRB, 100% for RBF, and 95% for BBF. A new model interpretability was assessed using explainability techniques, which allowed for the identification of specific learning patterns. This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. Additionally, distinct convolutional layers were associated with each fault type: layer 9 for RAF, layer 13 for BRB, layer 16 for RBF, and layer 14 for BBF. This methodology offers a scalable solution for predictive maintenance in induction motors, effectively combining signal processing, computer vision, and explainability techniques.

Broken Rotor Bar Fault Detection in Induction Motor Based on Spectral Analysis

Three-phase induction motors are among the most frequently used motors in industrial areas due to their simple structure, low cost, power specifications, etc. Electric energy consumption from these motors accounts for 68% of the energy used by all motors. The faults that occur in these motors over time decrease motor efficiency and result in significant energy consumption. In this study, a new method based on spectral subtraction (SS) was suggested for determining broken rotor bar (BRB) faults in these motors. The traditional method of motor current signature analysis via Fast Fourier Transform (FFT) is hard to use to diagnose BRB faults because the sideband characteristics used as a fault indicator are also seen in the healthy state at low amplitude levels. In the proposed fault detection method, the FFT of both healthy case and faulty case current signals were calculated and then the SS signal is obtained by subtracting the FFT of the healthy motor from the faulty motor for each time step. In the residual SS signal, fault detection was performed by examining the amplitude levels of the harmonic component of the BRB fault. Experimental results indicate that BRB faults can be successfully detected in squirrel-cage rotor induction motors using the suggested method.

Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network

The use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the detection, the higher the economic loss reduction. For this reason, this work proposes the development of a new methodology based on infrared thermography and an artificial intelligence-based classifier for the detection of multiple faults in an electromechanical system. The proposal combines the intensity profile of the grey-scale image, the use of Fast Fourier Transform and an artificial neural network to perform the detection of twelve states for the state of an electromechanical system: healthy, bearing defect, broken rotor bar, misalignment and gear wear on the gearbox. From the experimental setup, 50 thermographic images were obtained for each state. The method was implemented and tested under different conditions to verify its reliability. The results show that the precision, accuracy, recall and F1-score are higher than 99%. Thus, it can be concluded that it is possible to detect multiple conditions in an electromechanical system using the intensity profile and an artificial neural network, achieving good accuracy and reliability.

Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning

Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning

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.

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.

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%.

Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM

This study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and torque, the framework aims to effectively detect and classify broken rotor bars (BRBs) within inverter-fed induction machines. In this kind of cross-validation method, class labels and grouping factors are spread out across folds by distributing motor operational data attributes equally over target label stratification and extra grouping information. By integrating SSAE and LightGBM, a gradient-boosting framework, we elevate the precision and efficacy of defect diagnosis. The SSAE feature extraction algorithm proves to be particularly effective in identifying small BRB signatures within motor operational data. Our approach relies on comprehensive datasets collected from motor systems operating under diverse loading conditions, ranging from 0% to 100%. Using a sparse stacked autoencoder, the model lowers the dimensionality and noise of the motor fault data. It then sends the cleaned data to the LightGBM network for fault diagnosis. LightGBM leverages the attributes of the sparse stacked autoencoder to showcase the distinctive qualities associated with BRBs. This integration offers the potential to improve defect identification by furnishing input representations that are both more precise and more concise. The proposed model (SSAE with LightGBM) was trained using 80% of the data, while the remaining 20% was used for testing. To validate the proposed architecture, we evaluate the accuracy, precision, recall, and F1-scores of the results using motor global signals, with the help of confusion matrices with receiver operating characteristic (ROC) curves. Following the training of a new LightGBM model with refined hyperparameters through Bayesian optimization, we proceed to conduct the final classification utilizing the optimal feature subset. Evaluation of the test dataset indicates that the BRBs diagnostic framework facilitates the detection and classification of issues with induction motor BRBs, achieving accuracy rates of up to 99% across all loading conditions.

Broken Rotor Bars Detection in Inverter-Fed Induction Motors Under Continuous Switching of Different Speed Modes

Broken rotor bars (BRBs) fault detection of inverter-fed induction motors operating in transient regime is difficult, and even more challenging under continuous switching of constant speed, acceleration, and deceleration modes (CADM). In this condition, the BRBs caused amplitude-modulation of fundamental harmonic can be used as a fault indicator. However, the amplitude and frequency of the fundamental harmonic vary widely in continuous switching of CADM, which causes tremendous difficulty in the fault feature extraction. In this paper, a novel approach called Adaptive Window Short-time Esprit (AWSTE) is developed to overcome the described problem. It can track the fundamental frequency and extract the envelope of fundamental harmonic under continuous switching of CADM. Experimental results demonstrate the validity of the proposed approach at different control modes, different load levels, and different noise levels.

Identification between broken rotor bars and low-frequency load torque fluctuations in a three-phase induction motor

Identification between broken rotor bars and low-frequency load torque fluctuations in a three-phase induction motor

Identification between broken rotor bars and low-frequency load torque fluctuations in a three-phase induction motor

Identification between broken rotor bars and low-frequency load torque fluctuations in a three-phase induction motor

Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to broken bar fault

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.

Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to stator winding fault

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.

Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks

Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application.