Rotor Bars In Induction Motors Research Articles

Multiple-Kernel MRVM With LBFO Algorithm for Fault Diagnosis of Broken Rotor Bar in Induction Motor

Induction motors are key equipments widely used in modern industries. Fault diagnosis of broken rotor bar (BRB) timely and accurately is very important to ensure the reliable operation of induction motors (IMs). In this study, a multivariate relevance vector machine with multiple Gaussian kernels (MKMRVM) and principal component analysis (PCA) are developed to construct a classification model. Then an improved bacterial foraging optimization combining with Levy fight mechanism, named LBFO, is employed to tune the kernel parameters of MKMRVM to obtain the optimal fault diagnosis model. Finally, The LBFO-based MKMRVM classification model is used to identify the diagnosis of BRB of IMs. The performance is assessed based on a comprehensive experiment of fault diagnosis of BRB. The experimental results demonstrate that the proposed method may achieve high diagnosis accuracy for different noise levels of diagnosis signals and is superior to other related fault diagnosis techniques.

Effects of induction motor end ring faults on motor performance. Experimental results

Abstract Induction motors are inherently robust in construction, although faults can occur inside or outside in the motor due to various reasons. These failures cause deterioration of the motor performance and it can even cause the motor to be disabled. As a result of literature studies, it has been seen that a hole is opened in the middle part of the rotor bars for the induction motor rotor bar failures. In this study, different the studies in the literature, faults were introduced on the end ring part and on the intersections of the end ring and the rotor bars. The aim of this paper, end ring faults was investigated the effect of a squirrel cage induction motor on the motor performance. Seven motors with the same power and physical characteristics were used in the study. One of the motors is used as a reference (healthy) motor and the other six motors which have got with various faults in the end ring part. The three motors were used exactly as the faulty motor at the intersection of the end ring and the rotor bars, and the other three as the faulty motor which caused a scraped on the end ring part. No-load, locked-rotor and loaded operation tests were performed separately for each motor. The core losses, copper losses, current, power factor, efficiency, torque-speed characteristic and etc. values were obtained for seven motors. The motors that are tested are squirrel cage, 3 phase and 4 kW power. The obtained results are presented in comparative tables and graphs. Core loss and copper losses have increased by 21.57% for 3 scrapped motor and 9.83% for 2 broken rotor bar motor respectively, compared to the healthy motor. The efficiency value is reduced by 3.4% for the 3 scraped motor.

Fourier transform and image processing for automatic detection of broken rotor bars in induction motors

In the literature, many research studies have proposed the diagnosis of the induction motor condition where both the electrical and mechanical faults have been considered. Despite obtaining promising results, the diagnosis is mostly achieved qualitatively, requiring an expert user to interpret the results. This disadvantage could lead to time delays and additional costs that at an inopportune stage could prevent the diagnosis of the motor. In this study, a methodology based on signal processing and image processing is proposed to automatically diagnose a broken rotor bar (BRB) by using current signals. For the signal processing, first a decimation stage is proposed and then short-time Fourier transform is applied to the current signal. The proposed signal processing eliminates the 60 Hz component of the power line and its associated leakage. Next, the fault diagnosis is automated by applying image processing algorithms to the time-frequency plane of the current signal. From this time-frequency plane, the region of interest, in this case, the V-shaped pattern associated with the BRB condition, is automatically located mainly by using mathematical morphology-based algorithms. In addition, the area of the V-shaped pattern is also computed in order to automatically distinguish between the faulty and healthy condition, half BRB, one BRB, and two BRB. This last step of the method avoids the need of an expert user. For the area values, an analysis of variance is performed, where a 100% effectiveness is obtained for automatically determining the motor condition.

Higher-Order Statistics applied to machine learning as an approach to identify broken rotor bars in induction motors

Induction motors are reported as the horse power in industries. Due to its importance, researchers have been studied how to predict its faults in order to improve reliability. Condition health monitoring plays an important role in this field, since it is possible to predict failures by analyzing its operational data. This paper proposes the usage of vibration signals, combined with Higher-Order Statistics (HOS) and machine learning methods to detect broken bars in a squirrel-cage three-phase induction motor. The Multi-Layer Perceptron and Optimum-Path Forest have presented as promising approaches for faults classifications in an induction motor.

A Neuro-Fuzzy Approach for Locating Broken Rotor Bars in Induction Motors at Very Low Slip

Squirrel-cage induction motors are widely used in a number of applications throughout the world. This paper proposes a neuro-fuzzy approach to identify and to classify a typical fault related to the induction motor damage, such as broken rotor bars. Two fuzzy classifiers are obtained by an adaptive-network-based fuzzy inference system model whose parameters can be identified by using the hybrid learning algorithm. A Hall effect sensor was installed between two stator slots of the induction machine, and a magnetic flux density variation is measured according to the failure. The data from the Hall sensor were used to extract some harmonic components by applying fast Fourier transform. Thus, some frequencies and their amplitudes were considered as inputs for the proposed fuzzy model to detect not only adjacent broken bars, but also noncontiguous faulted scenarios. In the present work it is not necessary to estimate the rotor slip, as required by the traditional condition monitoring technique, known as motor current signature analysis. This method was able to detect broken bars for induction motor running at low-load or no-load condition. The intelligent approach was validated using some experimental data from a 7.5-kW squirrel-cage induction machine.

A strategy for broken bars diagnosis in induction motors drives

This paper presents a strategy for detecting broken rotor bars in induction motors (IM), using a zero sequence high-frequency signal injection. The derivatives of the motor currents are analyzed, the subsequent signal processing allows us to study how these signals change its value as an effect of a broken rotor bar in the IM. The main feature of the strategy is that the zero sequence high-frequency signals are generated by a standard space vector modulation of the IM drive. Analytical and experimental results are presented, validating the broken bars detection strategy.

Automatic progressive damage detection of rotor bar in induction motor using vibration analysis and multiple classifiers

There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and autocorrelation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, comparing the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.

Introducing the Filtered Park’s and Filtered Extended Park’s Vector Approach to detect broken rotor bars in induction motors independently from the rotor slots number

The Park’s Vector Approach (PVA), together with its variations, has been one of the most widespread diagnostic methods for electrical machines and drives. Regarding the broken rotor bars fault diagnosis in induction motors, the common practice is to rely on the width increase of the Park’s Vector (PV) ring and then apply some more sophisticated signal processing methods. It is shown in this paper that this method can be unreliable and is strongly dependent on the magnetic poles and rotor slot numbers. To overcome this constraint, the novel Filtered Park’s/Extended Park’s Vector Approach (FPVA/FEPVA) is introduced. The investigation is carried out with FEM simulations and experimental testing. The results prove to satisfyingly coincide, whereas the proposed advanced FPVA method is desirably reliable.

Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter

A comprehensive study of intelligent tools used to classify broken rotor bars in induction motors, which operate with three different types of frequency inverters, is presented. The diagnosis of defective rotor bars is a critical issue for the predictive maintenance of induction motors. A proper classification of these defects in their early stages of evolution is necessary for preventing major machine failures and production downtime. The proposed approach is performed by analysing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate based on machine frequency supply. To assess classification accuracy under the various severity levels of the faults, the performance of four different learning machine techniques is investigated: (i) fuzzy ARTMAP network; (ii) support vector machine (sequential minimal optimisation); (iii) k-nearest neighbour; and (iv) multilayer perceptron network. Results obtained from 1274 experimental tests are presented in order to validate the study, which considers a wide range of load conditions and operating frequencies. Experimental results presented in this study validate the robustness and efficacy of the proposed approach.

Detecting broken rotor bars in induction motors with model-based support vector classifiers

We propose a methodology for testing the sanity of motors when both healthy and faulty data are unavailable. More precisely, we consider a model-based Support Vector Classification (SVC) method for the detection of broken bars in three phase asynchronous motors at full load conditions, using features based on the spectral analysis of the stator's steady state current (more specifically, the amplitude of the lift sideband harmonic and the amplitude at fundamental frequency). We diverge from the mainstream focus on using SVCs trained from measured data, and instead derive a classifier that is constructed entirely using theoretical considerations. The advantage of this approach is that it does not need training steps (an expensive, time consuming and often practically infeasible task), i.e., operators are not required to have both healthy and faulty data from a system for checking it. We describe what are the theoretical properties and fundamental limitations of using model based SVC methodologies, provide conditions under which using SVC tests is statistically optimal, and present some experimental results to prove the effectiveness of the suggested scheme.

A fault detection scheme based on minimum identified uncertainty bounds violation for broken rotor bars in induction motors

In this paper, a novel method for broken bars fault detection in the case of three-phase induction motors and under different payloads will be presented and experimentally evaluated. In the presented approach, the cases of a partially or full broken rotor bars are being also considered, caused by: (a) drilling 4mm and 8mm out of the 17mm thickness of the same rotor bar and (b) fully drilled (17mm) one, two and three broken bars. The proposed fault detection method is based on the Set Membership Identification (SMI) technique and a novel proposed minimum boundary violation fault detection scheme, applied on the identified motor's parameters. The system identification procedure is being carried out on the simplified equivalent model of the induction motor, during the steady-state operation (non-fault case), while at the same time the proposed scheme is able to calculate on-line the corresponding safety bounds for the identified variables, based on a priori knowledge of the measuring corrupting noise (worst case encountered). The efficiency, the robustness and the overall performance of the established fault detection scheme is being extensively evaluated in multiple experimental studies and under various time instances of faults and load conditions.

Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but also how early these failures can be detected. The purpose of this paper is to present an experimental study of partially damaged rotor bar in induction motor under different load conditions based on discrete wavelet transform analysis. The approach is based on the extraction of features from vibration signals at different level of damage and three mechanical load conditions. The proposed analysis is reliable for tracking the damage in rotor bar. The paper presents an analysis and extraction of vibration features for partially damaged rotor bar in induction motors. The experimental analysis shows the change in behavior of vibration due to load condition and progressive damage.

A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors

One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Many of them rely on the qualitative interpretation of the results, a fact that requires significant user expertise, and that makes their implementation in portable condition monitoring devices difficult. In this paper, we present an automated method for the detection of the number of broken bars of an induction motor. The method is based on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic component that emerges once a rotor bar is broken. After the isolation of this component, a number of stages are applied that transform the continuous-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a representation that can be easily manipulated by a nearest neighbor classifier. The approach is tested using simulation as well as experimental data, achieving high-classification accuracy.

A Hybrid Technique for the Detection of Broken Rotor Bar of Induction Motor

A hybrid technique for diagnosing broken rotor bar fault of induction motor using Multi-Wavelet Transform (MWT) and radial basis neural network is presented. The stator currents of induction motor are preprocessed using multi-wavelet transform and the decomposed components are obtained. Then, these features are given as input to the neural network to identify fault. This paper compares the proposed hybrid technique with MWT-Feed Forward Neural Network (FFNN) and Discrete Wavelet Transform-FFNN techniques. These techniques are compared using the concept of classifier performance. From the simulation results, it is evident that the proposed method is superior to other methods with regard to objective proposed.

Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis

This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection.

Induction Motors Broken Rotor Bars Diagnosis Through the Discrete Wavelet Transform of the Instantaneous Reactive Power Signal under Time-varying Load Conditions

—In this article, a method based on the application of the discrete wavelet transform to the instantaneous reactive power signal, for diagnosing the occurrence of broken rotor bars ininduction motors operating under time-varying load conditions, is presented. This method is based on the decomposition of the instantaneousreactive power signal, from which wavelet approximation and detail coefficients are extracted. The energy evaluation of known bandwidths permits to define a fault severity factor. This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. These simulation results are complemented by experimental tests conductedon an induction motor with several faulty rotors that can be interchanged and both simulation and experimental results have shown the effectiveness of the proposed method for broken rotor bars diagnosis in induction motors even under time-varying load conditions.

Diagnosis of broken rotor bars in induction motors based on harmonic analysis of fault components using modified adaptive notch filter and discrete wavelet transform

This paper proposes a method for fast and accurate detection of broken rotor bars (BRBs) in a three-phase squirrel cage induction motor. The fundamental component of the stator current signal is extracted using a linear time-invariant filter. The resultant residual signal which contains the harmonic components of the current is then used to detect the BRBs, by means of discrete wavelet transform (DWT). Since in experiment it is not possible to break the rotor bars while the motor is under load, finite element method and MATLAB/Simulink are employed to accurately demonstrate the behavior of the running machine as the BRB happens. To get more accuracy, differential evolution (DE) optimization algorithm is used to obtain the corresponding fault impedance for the rotor external circuit of the MATLAB model. Detail coefficients (DCs) of the wavelet decomposition are employed as the new fault indicators. Simulation results show that using DCs of the harmonic component signal rather than the actual current signal, leads to more distinctive fault signatures in the wavelet decomposition. The obtained results suggest that the proposed fault detection scheme can be employed as a highly reliable technique for diagnosing rotor bar failures in running machines.

Detecting Broken Rotor Bars With Zero-Setting Protection

Broken rotor bars in induction motors can be dependably detected by analyzing the current signatures under sufficient motor load conditions. Detection becomes less dependable under light motor load conditions. There are also cases in which tolerable motor operating conditions generate current signatures similar to those of motors with broken rotor bars. These cases may present security concerns when the detection element is set to trip the motor and to send alarms. In this paper, we aim to achieve the following: show how broken rotor bars cause characteristic current signatures; show how to detect broken rotor bars with a zero-setting protection element, which uses the current signature method; use cases with different motor operating and fault conditions to analyze the performance of the zero-setting broken bar protection element; identify cases when the current signature method is dependable and cases when security is a concern, and present solutions to address security concerns.

DSP implementation of a novel envelope analysis approach for the diagnosis of broken rotor bar in induction motor

Design and implementation of a novel technique for the detection of broken rotor bar of the induction motor is presented. At first, the discrete wavelet transform (DWT) is performed on the windowed steady state stator currents and then discrete Hilbert transform is utilised to obtain envelopes of the detailed coefficients at higher wavelet level. It has been observed that the faulty motor produces higher statistical parameters – mean, rms, standard deviation and median of the envelopes than those for the healthy one. The method is implemented in real time by means of low cost TMS320VC5416 fixed point digital signal processor (DSP). A framework of assembly language program for the calculation of the DWT is given and the problems which should be paid attention during real time implementation are discussed. Illustrative laboratory test results confirm the validity and accurate performance of the proposed method.

A combined technique for rotor broken bar diagnosis in VSD equipped with vibration reducers

Broken rotor bar is a common fault in squirrel cage induction motors. Rotor failures due to that fault now account for a moderate percentage of total induction motor failures. This paper will deal with detection of broken rotor bar of induction motor supplied from variable speed drive (VSD) equipped with AC reactor and sine-wave shaping filter on the output side. The experimental results show that the instantaneous power spectrum analysis method is only effective when the motor is supplied from sinusoidal supply, in contrast for the demonstrated VSD. The instantaneous power spectrums of the healthy rotor case and the broken bar rotor cases is processed with mean absolute difference (MAD) approach algorithm to investigate the dissimilarity between them. The results show that this combined technique is highly effective in the diagnosis of broken rotor bar fault in case of the demonstrated VSD.