Broken Rotor Bar Fault Research Articles

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.

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

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.

Rotor fault characterization study by considering normalization analysis, feature extraction, and a multi-class classifier

Background. Rotor faults are the most common malfunctions encountered, especially during the manufacturing stage, in asynchronous motors. These faults cause vibration in the motor torque and a decrease in efficiency. In recent years, the detection of rotor faults has been done using motor current. The reflection of rotor faults on motor current depends on slip, and therefore, the effect increases as the current grows. Good results are achieved in fault detection at nominal loads. However, especially when motor manufacturers are considered, testing the motor by loading it requires expensive testing equipment and long-term test procedures. Therefore, the detection of faults in the motor at no load is emphasized. However, since the effect of the fault decreases when the motor is at no load, fault detection becomes difficult. Generally, small-level faults cannot be detected. Objective. This study focuses on fault detection from the motor current at no load. The development current at no load was used to eliminate the negative effects of slip. However, since the slip is not constant, the change in frequency and amplitude values to be used as a feature makes the diagnosis difficult. Method. In this study, the spectrogram was used to evaluate the change during the start-up time. Thus, a standard dataset was determined for comparison. The texture properties of the spectrogram image were extracted using various methods. The extracted features were subjected to normalization analysis and classified using the k-NN algorithm. Results. In the classification phase, a classification accuracy of 98.66% was achieved using the k-NN method, and it was seen that the proposed method could be used for the detection of rotor faults. Conclusions. The study has successfully demonstrated that broken rotor bar faults in asynchronous motors can be diagnosed using the motor start-up data.

Analysis of a Three-Phase Induction Motor with a Double–Triple-Layer Stator Winding Configuration Operating with Broken Rotor Bar Faults

This paper presents the performance analysis of a three-phase squirrel cage induction motor (SCIM) with a double–triple-layer (DTL) stator winding configuration operating with broken rotor bar (BRB) faults. The effects of BRB faults on the performance of specific parameters are analyzed under a steady-state regime. The SCIM is modeled using the two-dimensional finite element method (FEM) to study electromagnetic performance under healthy and BRB faulty conditions. To validate the finite element analysis (FEA) results, a prototype of an SCIM with a DTL stator winding configuration is tested for performance evaluation under healthy and BRB faulty conditions. The FEA and experimental (EXP) results of the SCIM with a DTL stator winding arrangement are compared with the results of the SCIM with a conventional double-layer (CDL) stator winding configuration. FEA and EXP results evidenced that the SCIM with a DTL stator winding configuration mitigates some of the adverse effects introduced by the BRB faults compared to the SCIM with a CDL stator winding of the exact specifications. Under loaded conditions, the SCIM with a DTL stator winding configuration reduced the magnitudes of the twice slip frequency sidebands caused by BRB faults from ±1.2 Hz for the SCIM with a CDL stator winding arrangement down to ±0.2 Hz and ±0.36 Hz when operating with 3BRB and 6BRB faults, respectively. The results also indicate that the SCIM with a DTL stator winding configuration has reduced the decibel sideband magnitude by 7.5 dB and 8 dB for unloaded and loaded conditions, respectively. This premise has positioned the SCIM with a DTL stator winding configuration as a strong candidate in applications where BRB faults are frequent, and the motor may be required to continue operating with a BRB fault until scheduled maintenance is in effect.

Detection of Broken Rotor Bars in Cage Induction Motors Using Machine Learning Methods.

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 time-frequency method for induction motor fault feature extraction using instantaneous power signals

To solve the issue that the characteristic frequencies of the broken rotor bar fault (BRBF) are always submerged by the fundamental component during the stator current spectrum analysis in light loads, this paper proposes an effective approach based on the instantaneous power signal and the local maximum synchrosqueezing transform (LMSST). By multiplying the current and voltage, the instantaneous power signal can provide more fault features and enhance fault component amplitudes. The LMSST method is used to obtain a time-frequency analysis with more concentrated energy, and more fault characteristics are extracted from the reconstructed signal, which helps to accurately identify the motor BRBF.

Wavelet packet analysis for rotor bar breakage in an inverter induction motor

Introduction. In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the low-load, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.

Modeling and Simulation of Faulty Induction Motor in DQ Reference Frame Using MATLAB/SIMULINK with MATLAB/GUIDE for Educational Purpose

Owing to their robust structure, induction motors are preferred to be used under difficult working conditions. Therefore, various faults may occur in the motor due to unexpected forces during the operations. Obtaining the data through experimental methods by physically creating faults in the induction motors, and analyzing their behavior is not efficient in terms of cost and time for educational purposes. Considering the above negative situation, in this paper, mathematical models have been developed in the dq0 stationary reference frame expressing three-phase stator windings short-circuit fault and broken rotor bar fault in induction motors. The proposed models are faster as compared to the other analytical models in terms of computation due to the rotor position independence of the inductance matrix. The faulty induction motor mathematical models have been implemented in the MATLAB/SIMULINK software environment with detailed explanations of each faulty model’s subsystem. As a visual laboratory that can be used as an educational tool for the analysis of a three-phase faulty induction motor, a graphical user interface application has been developed in MATLAB/GUIDE, which allows users to simulate models from a single interface. As a case study, the behaviors of faulty induction motors in transient and steady states have been simulated in different severity scenarios of the faults. The park vector method has been used as a fault diagnosis approach to investigate fault types and the fault severity effects on the park vector pattern in each fault scenario. In addition, to observe the success of the developed Simulink toolboxes, they were used at Marmara University through courses in electrical machinery and evaluated by the graduated students at the end of the semester.

A novel dataset and lightweight detection system for broken bars induction motors using optimizable neural networks

Broken rotor bars (BRBs) in induction motors (IMs) are a common kind of failure and one of the most difficult to detect since the induction motor continues to run properly in the absence of any evidence of a malfunction. There have been several approaches presented for identifying BRBs in IMs. However, in order to parameterize the current signal, they need tools with a high computational cost, making an online implementation of the appropriate monitoring system difficult. As a result, this paper proposes a novel AI-based approach to detect and classify BRBs faults in IMs using novel multi-class datasets from two three-phase, 400V induction motors; 2.2kW, two poles, 50 Hz, 24 stator slots and with 20,29 and 32 rotor bars numbers (M24Ns,(20,29,32)Nb), and 2.2kW, four poles, 60 Hz, 36 stator slots with 28 and 44 rotor bars numbers (M36Ns, (28,44)Nb) to help in the analysis of healthy and faulty IMs, as well as classification performance evaluation and benchmarking. The developed lightweight, intelligent, and autodetection system employs a self-configurable neural network model for BRBs in IM models. It provides four classification outputs: healthy, one-BRB, two-BRBs, and three-BRBs. The simulation results characterize the performance of the (M24Ns, (20,29,32) Nb) and (M36Ns, (28,44) Nb) IMs and the combined mode for both motors, demonstrating that the proposed approach is very effective in detecting and classifying BRBs, with a 99.8 % and a prediction time of 1.64 microseconds.

Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square

This study proposes a broken rotor bar (BRB) fault diagnosis method for an induction motor using the sum of the weighted Fourier series coefficients squares of a complex current as a diagnosis signal. First, the sum of the squares of the Fourier series coefficients confirms the very narrow band-pass filter characteristics to derive a specific frequency component. This assists us in obtaining a BRB fault diagnosis signal that exists in a limited frequency range. Second, the magnitude of the Fourier series coefficients of the BRB fault signal is proportional to the slip frequency and load condition. A weighting factor is proposed to render the BRB fault signal irrelevant to the slip frequency and load condition. Consequently, the proposed fault diagnosis can be conducted without the slip frequency information or searching for the maximum coefficient component. Finally, the proposed fault diagnosis method is validated through experiments using a 55 kW induction motor with and without a BRB fault. It is implemented with a DSP controller at time intervals of 20, 10, 5, and 4 s for the Fourier series. The proposed diagnosis method performs well under various load conditions and shows that the derived fault signal exhibits a large difference between healthy and BRB faulty induction motors.

Stochastic Event-Triggered Fault Detection and Isolation Based on Kalman Filter.

In this article, the robust fault detection and isolation (FDI) Kalman filter is extended based on stochastic event-triggered schedulers for discrete linear systems consisting of deterministic/stochastic unknown inputs with nonzero mean and colored measurement noise. In the proposed FDI method, first, a subspace of the main system that significantly attenuates disturbance effects is proposed. After that, the fusion method is proposed for dealing with the colored measurement-noise problem in designing the Kalman filter and preventing from leading to measurement noise with zero mean and zero covariance. The stochastic event-triggered schedulers are considered as Gaussian functions. Therefore, the Gaussian property of innovation sequence is preserved, and consequently, the recursive equation of the Kalman filter need not be extended based on approximation techniques. Finally, design parameters are chosen based on the convex optimization problem such that the lowest communication rate between the sensor node and FDI filter, and also the best FDI performance are achieved. The proposed FDI method is evaluated to detect and isolate stator interturn short circuit and broken rotor bars faults with unbalanced voltage as disturbance in three-phase induction motors.

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.

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.

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.

Treatment Strategy Research on a Squirrel-Cage Induction Motor with Broken Rotor Bar Faults.

Squirrel-cage induction motors are increasingly displaying a broken rotor bar fault, which represents both a technical problem and an economic problem. After confirming that the broken rotor bars do not affect the normal start-up and basic working performance of the squirrel-cage induction motor, this paper focuses on the loss and efficiency changes of the motor brought about by the broken rotor bar fault. Using finite element simulation and experimentation, various losses like stator copper loss, iron loss, rotor copper loss, mechanical loss and additional losses, total loss and efficiency are obtained. By combining price and cost factors, the cost-effective measures that can be taken after the occurrence of different degrees of broken bars are evaluated here to provide guidance for correctly handling this problem.

Induction Motor Broken Rotor Bar Fault Diagnosis Based on Third-Order Energy Operator Demodulated Current Signal

Motor current signature analysis (MCSA) has been widely used for fault diagnosis of induction motors (IMs) because of its non-invasiveness and comparatively low cost. However, the spectral leakage of the fundamental supply frequency can obscure the fault characteristic component when IMs operating at no-load or low-load conditions. In this paper, a broken rotor bar (BRB) fault diagnosis approach based on the third-order energy operator (TOEO) demodulated current signals is presented. The TOEO approach performs effectively in reducing the negative impact of the fundamental supply frequency spectral leakage and enhancing the BRB fault features. Theoretical and experimental results show that the TOEO approach can excellently reveal the fault characteristic frequency, instead of the sideband frequencies. Besides, the results are compared with the Teager-Kaiser energy operator (TKEO). The capability of the presented approach has several advantages over the TKEO, especially when the IMs are fed by an inverter.

Wavelet packet and fuzzy logic theory for automatic fault detection in induction motor.

In this paper, a method based on the application of the fuzzy logic technique to diagnose the fault of broken rotor bars in an induction machine has been proposed. Through the decomposition into a wavelet packet, we can detect, identify and prognosis failures in all operating conditions of the machine. The energy calculations for each level of decomposition are richer with the necessary information for fault diagnosis. The latter can be used as input to an intelligent diagnostic system based on fuzzy logic for the detection and classification of the broken bars faults. The advantage of this method is the use of a single current sensor. Indeed, we can detect online, the fault and the number of broken bars with a variable load. The obtained results are very satisfactory. Some results were verified by simulations under MATLAB/Simulink and validated experimentally via dSPACE 1104 card.

Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction

This paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Experimental tests have been set up using a 1.5 Hp/380 V three-phase SC-IM with different combined ITSC and BRB faults under different loading conditions. Before entering the model, the Discrete Wavelet Transform (DWT) pre-processes the stator current signal. The DWT generates data sets in order to evaluate the proposed technique. ANFIS based on DWT is used successfully to diagnose the most relevant faults very effectively. In addition, ANFIS based on the DWT method has been compared to ANFIS and ANFIS based on an auto-regressive model, finding that the proposed method achieves higher efficiency than the previous one. The proposed ANFIS based on the DWT model classifies entirely different states of combined ITSC and BRB faults with high accuracy.

Applicability Analysis of Indices-Based Fault Detection Technique of Six-Phase Induction Motor

The multiphase induction motor is considered to be the promising alternative to the conventional three-phase induction motor, especially in safety-critical applications because of its inherent fault-tolerant feature. Therefore, the attention of many researchers has been paid to develop different techniques for detecting various fault types of multiphase induction motors, to securely switch the control mode of the multiphase drive system to its post-fault operation mode. Therefore, several fault detection methods have been researched and adapted; one of these methods is the indices-based fault detection technique. This technique was firstly introduced to detect open-phase fault of multiphase induction motors. The main advantage of this technique is that its mathematical formulation is straightforward and can easily be understood and implemented. In this paper, the study of the indices-based fault detection technique has been extended to test its applicability in detecting some other stator and rotor fault types of multiphase induction motors, namely, open-phase, open-switch, bad connection and broken rotor bar faults. Experimental and simulation validations of this technique are also introduced using a 1 kW prototype symmetrical six-phase induction motor.