Bars In Induction Motors Research Articles

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.

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

Rotor speed estimation for half-broken bar detection in induction motors using Kalman filtering

This study addresses the early detection of half-broken bars in induction motors (IMs). Most research on fault detection focuses on current signature analysis because it can provide online and remote monitoring at a low cost. However, errors caused by spectral leakage around the fundamental frequency supply reduce its reliability for fault diagnosis at the incipient stages. The proposed detection technique is based on a spectral analysis of the motor speed. To detect a half-broken bar, measurements of stator voltages and currents are processed by an adaptive extended Kalman filter for system state estimation. Then, a spectral analysis is applied to the estimated motor speed to obtain its power spectral distribution. Finally, the fault harmonic amplitude at 2sfs is used as fault indicator for fault diagnosis. The proposed approach has three main advantages: (1) speed estimation is unaltered by spectral leakage around the main frequency supply, (2) the cost of speed sensors is avoided, and (3) the absence of connecting shaft couplings prevents corruption of speed data by mechanical interference. Numerical simulations and experimental results were carried out for the squirrel cage IM to provide encouraging validation of the proposed early fault detection technique.

Accurate Criteria for Broken Bar Detection in Induction Motors Based on the Wavelet (Packet) Transform

Finding reliable and robust criteria for the detection of broken bars in induction motors is key for the maintenance of industrial engines, and some of the most efficient methods analyze the stator start-up current. Due to the transitory characteristics and short duration of the signal, suitable time-frequency mathematical tools are very useful for this purpose. We propose here algorithms based on the discrete wavelet and wavelet packet transform, combined with other tools in signal processing, to offer an accurate quantitative method for failure detection due to broken bars in induction motors. A good selection of the wavelet family is important for a good performance of the indicator, and the discrete approximation of the Meyer wavelet, ‘dmeyer’, consistently demonstrates the most favorable results. Our findings highlight the effectiveness of both the wavelet and wavelet packet transforms in accurately detecting broken bars in induction motors. This fact allows optimal monitoring strategies in industrial applications.

Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors.

Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.

Detection of Broken Bars in Induction Motors Using Histogram Analysis of Current Signals

The lifetime of induction motors can be significantly extended by installing diagnostic systems for monitoring their operating conditions. In particular, detecting broken bar failures in motors is important for avoiding the risk of short circuits or other accidents with serious consequences. In the literature, many approaches have been proposed for motor fault detection; however, additional generalized methods based on local and statistical analysis could provide a low-complexity and feasible solution in this field of research. The proposed work presents a methodology for detecting one or two broken rotor bars using the sums and differences histograms (SDH) and machine learning classifiers in this context. From the SDH computed in one phase of the motor’s current, nine texture features are calculated for different displacements. Then, all features are used to train two classifiers and to find the best displacements for faults and health identification in the induction motors. A final experimental evaluation considering the best displacements shows an accuracy of 98.16% for the homogeneity feature and a few signal samples used in a decision tree classifier. Additionally, a polynomial regression curve validates the use of 50 samples to obtain an accuracy of 88.15%, whereas the highest performance is achieved for 250 samples.

Expert System Based on Autoencoders for Detection of Broken Rotor Bars in Induction Motors Employing Start-Up and Steady-State Regimes

Induction motors are indispensable, robust, and reliable machines for industry; however, as with any machine, they are susceptible to diverse faults. Among the faults that a motor can suffer, broken rotor bars (BRBs) have become one of the most studied ones because the motor under this fault condition can continue operating with apparent normality, yet the fault severity can quickly increase and, consequently, generate the whole collapse of the motor, raising repair costs and the risk to people or other machines around it. This work proposes an expert system to detect BRB early, i.e., half-BRB, 1-BRB, and 2-BRB, from the current signal analysis by considering the following two operating regimes: start-up transient and steady-state. The method can diagnose the BRB condition by using either one regime or both regimes, where the objective is to somehow increase the reliability of the result. Regarding the proposed expert system, it consists of the application of two autoencoders, i.e., one per regime, to diagnose the BRB condition. To automatically separate the regimes of analysis and obtain the envelope of the current signal, the Hilbert transform is applied. Then, the particle swarm optimization method is implemented to compute the separation point of both regimes in the current signal. Once the signal is separated, the two autoencoders and a simple set of if-else rules are employed to automatically determine the BRB condition. The proposed expert system proved to be an effective tool, with 100% accuracy in diagnosing all BRB conditions.

Broken Bar Fault Detection Using Taylor–Fourier Filters and Statistical Analysis

Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on the motors, such as mechanical and electrical stresses, together with an increase in electricity consumption, causing higher operative costs and losses related to the maintenance times or even the motor replacement if the damage has led to a complete failure. To prevent such situations, diverse signal processing algorithms have been applied to incipient fault detection, using different variables to analyze, such as vibrations, current, or flux. To counteract the broken rotor bar damage, this paper focuses on a motor current signal analysis for early broken bar detection and classification by using the digital Taylor-Fourier transform (DTFT), whose implementation allows fine filtering and amplitude estimation with the final purpose of achieving an incipient fault detection. The detection is based on an analysis of variance followed by a Tukey test of the estimated amplitude. The proposed methodology is implemented in Matlab using the O-splines of the DTFT to reduce the computational load compared with other methods. The analysis is focused on groups of 50-test of current signals corresponding to different damage levels for a motor operating at 50% and 75% of its full load.

An Experimental Comparative Study Between Machine Learning and Signal Processing Techniques to Detect Broken Rotors Bars in Induction Motors

An Experimental Comparative Study Between Machine Learning and Signal Processing Techniques to Detect Broken Rotors Bars in Induction Motors

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.

Increasing Feasibility of Neural Network-Based Early Fault Detection in Induction Motor Drives

Modern industrial plants are complex and very sensitive to costs to the business of unscheduled downtime when a motor fails. This is the case of broken bars in induction motor (IM) drives, which still represents a large share of the market. In principle, an early defect detection is made possible by advanced artificial intelligence (AI)-based techniques, but their complexity clashes with the essential nature of IMs. This article aims to bridge the gap by using motor current signature already available in standard drives and proposing a mix of simulations and data augmentation to train efficiently the neural network (NN) without the need of many broken prototypes, which is the major flaw for the industrial feasibility.

Walsh–Hadamard Domain-Based Intelligent Online Fault Diagnosis of Broken Rotor Bars in Induction Motors

Mechanical failures are the most common defects in induction motors (IMs). They derive from the continuous starts and stops with high-load torque. They can cause interruptions in production lines with great economic losses. Therefore, online monitoring systems, allowing a continuous monitoring for fault diagnosis in early stages, have been intended for many researches in last years. Broken rotor bars (BRBs) are common failures, which are among the most difficult to detect since the induction motor works with apparent normality without giving any signal about the defect. Several methodologies have been proposed to detect BRBs in IMs. However, they require high-computational-complexity algorithms for parameterizing the current signal, making it difficult to achieve an online implementation of the corresponding monitoring system. Hence, aiming to overcome the limitations provoked by complex parameterizations of the current signal and to improve the fault classification accuracy, in this work, a novel methodology, based on a hardware processing system implemented on a field-programmable gate array (FPGA), for electric current signal analysis in the Walsh–Hadamard domain is proposed to achieve online intelligent BRB diagnosis. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN) algorithm is used for classifying different operational conditions from the Walsh–Hadamard transform (WHT) of the electric-current signal. Experimentally obtained results from applying the proposed method on acquired real data demonstrate that the introduced approach reaches a remarkable high efficiency on detecting and classifying BRBs.

Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor

The present study tries to propose a new method which is using Central deference Kalman Filter (CDKF) as input index of deep machine learning (DML), for simulating state estimation and broken rotor bars (BRBs) diagnosis in induction motors (IMs). In addition, an advanced selective ensemble (DML) method applying the grey wolf optimization (GWO) algorithm to diagnose the BRBs in IMs is proposed in the following study. Here, in the initial step, in order to take sufficient datum within the usual efficiency, it is needed to train the DML network. The outcomes indicate that the offered scheme can more accurately and powerfully detect diverse forms of BRBs with an accuracy of more than 98%. And also, Filter precision is enhanced via changing the sigma points of the filter, however, the stability of the filter enhances more because of its utilization, and the CDKF is further stable and precise in comparison to the Unscented Kalman filter (UKF). The CDKF performance is assessed to estimate the speed and is used as input index of DML to diagnose the broken rotor bar in IMs. The obtained outcomes prove the performance of this combined scheme.

A Novel Algorithm to Detect Broken Bars in Induction Motors

A new algorithm is proposed in order to detect and quantify partially broken bars in induction motors during start-up without load. In the qualification process, no threshold is used. It uses the principle of the harmonic generated by the broken bar in the stator current, it should vary with the slip to confirm the failure and provide more security in the diagnosis. A severity index is also proposed, based on the maximum peaks of the Teager energy operator of the Gaussian filter applied in the stator current signal. Experimental data were used to validate the algorithm, comparing rotors manufactured with one partially bar, one failed bar, and two completely failed bars, arranged in a variety of ways. The results show that the algorithm qualifies correctly the faulty bar, even for a partially broken bar. In the quantification phase, the severity index of the fault shows the higher sensibility in comparison to the state-of-the-art. Its value for a 3 HP motor is: 8.837 × 10−10 for a healthy rotor, 2.553 × 10−8 for a partially broken bar, and 4.058 × 10−7 for one broken bar.

Diagnosis of Defective Rotor Bars in Induction Motors

This paper proposes a diagnosis method, combining signal analysis and classification models, to the rotor defect problems of motors. Two manufacture technologies, nonmagnetic high-temperature resistant ceramic adhesive and electrical discharge machining (EDM), are applied to make testing samples, including blowhole and perforation defects of rotor bars in this study. The typical multiresolution analysis (MRA) model is used to analyze acquired source current signals of motors. The features are extracted from the signals of each column of MRA-matrix, including maximum, mean, standard deviation, root-mean-square, and summation. The typical back-propagation neural network (BPNN) model is used to diagnose the rotor bar defects of motors, and then the various signal-to-noise ratio (SNR) of white Gaussian noise (WGN), 30, 25, and 20 dB, are added to the signals to verify the robustness of the proposed method. The results show the availability of the proposed method to diagnose the rotor bar defects of motors.

Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors.

Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.

Fundamental Frequency Suppression for the Detection of Broken Bar in Induction Motors at Low Slip and Frequency

Broken rotor bar (BRB) is one of the most common failures in induction motors (IMs) these days; however, its identification is complicated since the frequencies associated with the fault condition appear near the fundamental frequency component (FFC). This situation gets worse when the IM slip or the operation frequency is low. In these circumstances, the common techniques for condition monitoring may experience troubles in the identification of a faulty condition. By suppressing the FFC, the fault detection is enhanced, allowing the identification of BRB even at low slip conditions. The main contribution of this work consists of the development of a preprocessing technique that estimates the FFC from an optimization point of view. This way, it is possible to remove a single frequency component instead of removing a complete frequency band from the current signals of an IM. Experimentation is performed on an IM operating at two different frequencies and at three different load levels. The proposed methodology is compared with two different approaches and the results show that the use of the proposed methodology allows to enhance the performance delivered by the common methodologies for the detection of BRB in steady state.

Voltage unbalance evaluation in the intelligent recognition of induction motor rotor faults

Induction motors are widely used in several industrial applications due to their factors of favouritism already consolidated, such as robustness, low cost and high reliability. Early detection and proper fault diagnosis reduce the maintenance cost and also increase process effectiveness. Therefore, this paper presents a method for fast classification of rotor faults in line-connected induction motors operating at steady state, under unbalanced voltages and load conditions. Hence, the amplitude of the stator’s current signal in the time domain is presented as input to intelligent computational models for the classification of rotor’s faults. After a proper discretization of the current signal, the points extraction technique is applied allowing a reduction in the classifier’s complexity. Results from 900 experimental tests are provided and compared to validate this study. The results indicate that this approach can be employed to proper classify rotor broken bars in induction motors operating under unbalanced voltage and different load conditions.

New Spectral Markers for Broken Bars Diagnostics in Induction Motors

The paper discusses the spectral markers of fault rotor bars in induction motor current signature analysis (MCSA). The results of the simulation of the deterioration process for a single rotor bar, as well as the results of research for various mutual bracing of two broken bars, are reported. We proposed a simple empiric technique allowing one to obtain frequencies for spectrum markers of damaged rotor bars based on simulation analysis. The set of frequencies obtained in the experimental part of the study was compared with simulation results and the results of real-life measurements. The theoretical results were verified through the experiment with the real induction motor under load. Analysis of experimental results proved that the given algorithm for spectrum analysis is suitable for early detection of fault rotor bars in induction motors.