Rotor Bars In Induction Motors Research Articles

Induction motor broken rotor bars detection using fuzzy logic: experimental research

This paper proposes a new automated practical method for the diagnosis and detection of broken rotor bars in induction motor. This method uses only one stator current sensor. It is based in spectral analysis of stator current envelope (SCE) obtained via Hilbert transform. According to the fault diagnosis objective, two features are selected from the SCE spectrum: the amplitude of the 2sf harmonic (s is the slip and f the fundamental harmonic) representing the broken bars defect and the DC value. These features will be used as inputs of fuzzy logic block, where the decision about the state of rotor is made. The results obtained are very satisfactory and the diagnostic system is capable to detect the correct number of broken bars.

Development of a Test Bed for Examination of an Induction Motor with Broken Rotor Bars

Examining a failed induction motor is a complex problem. It is very important to monitor induction motor faults such as broken rotor bar or end ring fault under the laboratory conditions. Since such faults do not frequently occur, they have to be created by the researcher himself or herself. Induction motor rotor bar faults are generally created by drilling holes to the rotor bars or to the end rings. Transient analyses are carried out both for the faulted and healthy conditions. In this study, an experimental test bed was developed to examine the experienced broken rotor bar faults for induction motors. By using the developed test bed, all possible combinations of broken rotor bar faults can be created. This test bed can successfully be used to model different fault types. In the case of broken rotor bar faults, extensive examination of motor behaviors for training purposes will also be possible. Also for faulted and healthy conditions by performing no-load and blocked rotor test experiments the changes in equivalent circuit parameters have been investigated. Moreover the importance of transition resistance and its effects on motor performance has been emphasized

Simplified detection of broken rotor bars in induction motors controlled in field reference frame

In this paper, a simple method for additional on-line detection of broken rotor bars in a squirrel cage induction motor controlled in rotor field co-ordinates using existing hardware is presented. Based on a previously presented approach, an algorithm for on-line calculation of the variance of stator voltage reference, which depends on the number of broken bars, has been developed. Due to its simplicity, it could run in parallel with a standard control algorithm in field reference frame using contemporary fixed- and floating-point processors, thus requiring minimum processing time. The algorithm uses internal reference values of the stator voltage; therefore no additional dedicated measurements are needed. Results were obtained at different operating points on an induction motor with gradually damaged rotor. Comparison with commonly used diagnostic method confirms the validity of the approach.

Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform

This paper introduces novel indices for broken rotor bars diagnosis in three-phase induction motors based on wavelet coefficients of stator current in a specific frequency band. These indices enable to diagnose occurrence and determine number of broken bars in different loads precisely. Besides thanks to the suitability of wavelet transform in transient conditions, it is possible to detect the fault during the start-up of the motor. This is important in the case of start-up of large induction motors with long starting time and also motors with frequent start-up. Furthermore, broken rotor bars in induction motor are detected using spectra analysis of the stator current. It is also shown that rise of number of broken bars and load levels increases amplitude of the particular side-band components of the stator currents in the faulty case. An induction motor with 1, 2, 3 and 4 broken bars at the rated load and the motor with 4 broken bars at no-load, 33%, 66%, 100% and 133% rated load are investigated. Time stepping finite element method is used for modeling broken rotor bars faults in induction motors. In this modeling, effects of the stator winding distribution, stator and rotor slots, geometrical and physical characteristics of different parts of the motor and non-linearity of the core materials are taken into account. The simulation results are are verified by the experimental results.

Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

A New Portable, Self-Powered, and Wireless Instrument for the Early Detection of Broken Rotor Bars in Induction Motors

The detection of rotor asymmetries in squirrel-cage induction motors has been an important research topic during the last two decades that has given rise to the development of various diagnostic techniques. Despite these techniques being reliable and providing much information about a machine's state, they still lack the capability to detect a failure when the motor is driving a machine, producing oscillations in the load torque. If this happens, then the motor phase currents are modulated by torque oscillations and the information that they contain about the integrity of the bars and endrings is no longer correct. Currently, the authors are not aware of a simple and reliable method that exists for the industrial diagnosis of rotor asymmetries in working cage motors under arbitrary load conditions. This paper describes a new diagnostic method and a new portable electronic instrument that are based on the measurement of the magnetic flux linked by one stator tooth, which allows for an optimal simple discrimination between the actual presence of rotor asymmetries and the spurious effects that are caused by the oscillations in the load torque of the driven machine.

Fault Signature Analysis Based on Transient Current for Induction Motors

The characteristics of broken rotor bars in induction motors are reflected in the abnormal harmonic of the stator current. At present, fast Fourier transform( ) and time-varying frequency spectrum analysis method are used in such fault diagnosis, but non-stationary motors operation can bring a certain difficulties to the monitoring and diagnosis. This paper studies the basic characteristics of wavelet transform, adopting the wavelet analysis technologies of signal processing and selecting mother wavelet, the paper makes the multi-scale transformation to the motor starting current, excavates the harmonic informations on non-stationary condition, realizes fault diagnosis of motor broken rotor bars effectively, The consistent diagnostic results prove the effectiveness of the method.

Discussion of “A New Criterion for the Quantification of Broken Rotor Bars in Induction Motors”

The paper proposes an improvement on the motor current signature analysis diagnostic procedure for induction machines broken rotor bars detection. The procedure, referred to as the Filippetti criterion in the paper, is based on the sum of the sidebands amplitude.

Closure on “A New Criterion for the Quantification of Broken Rotor Bars in Induction Motors”

We review the corresponding references and find out that there exists a mess indeed. The angular displacement refers to the stator winding impedance angle. It is worth mentioning that the power factor angle approximately equals to the equivalent circuit impedance angle disregarding the rotor asymmetry resulted from an incipient rotor fault. Consequently, it is reasonable to regard the angular displacement as the power factor angle.

Robust condition monitoring for early detection of broken rotor bars in induction motors

Cage rotor induction machines are a vital component in many industrial processes and other economic sectors where an unpredicted shutdown can be very costly. Therefore, an adequate warning of incipient faults via condition monitoring is very interesting for these applications. In this paper, we propose a condition monitoring technique based on robust statistical tools to detect incipient faults in induction motors related to asymmetries in the rotor cage. This technique uses the Fast Fourier Transform to obtain the spectrum of the motor line current and then, a multiresolution technique using wavelet functions is applied to this spectrum in order to detect significant peaks and to measure the height of these peaks with respect to the “baseline” signal. Finally, a Quality Control approach based on robust multivariate control charts is applied to detect a progressive deterioration of the rotor cage. To show the usefulness of the proposed method, we present a case-study in which a cage fault condition was provoked by drilling a hole in one of the bars of an induction motor. The different fault conditions were obtained by progressively making the hole deeper and a great deal of laboratory tests were performed for each fault condition.

Sliding mode observer for on-line broken rotor bar detection

The major task for any monitoring system is to detect upcoming faults as early as possible. Rotor failures are responsible for a large percentage of total induction motor failures. Thus, a new nonintrusive and in-service approach has been proposed in this paper to detect one broken rotor bar in induction motor using only input quantities information. The method is not affected by the type of load and other asymmetries and it is capable of providing reliable fault diagnose without maintenance specialist intervention. The proposed sensorless technique is based on two real time state space discrete models that are used to estimate the flux of both the stator and the rotor and analyzes the differences obtained in torque when the two models are employed. One of the observers was designed for rotor resistance disturbance rejection. Firstly, simulation results have been conducted and sensitivity and robustness also have been checked for the proposed method. Secondly, an experimental setup has been constructed to implement the new technique in on-line model. The results obtained validate the proposed model and show the reliability of the new method.

Detection of broken rotor bars in induction motors using nonlinear Kalman filters

This paper presents a model-based fault detection approach for induction motors. A new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is utilized as a state estimation tool for on-line detection of broken bars in induction motors based on rotor parameter value estimation from stator current and voltage processing. The hypothesis on which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. In order to compare the estimation performance of the EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. Computer simulations are carried out for a squirrel cage induction motor. The results show the superiority of UKF over EKF in nonlinear system (such as induction motors) as it provides better estimates for rotor fault detection.

A New Criterion for the Quantification of Broken Rotor Bars in Induction Motors

This paper addresses itself to evaluate quantitatively the severity of broken rotor bar (BRB) fault, i.e., the number of BRBs in induction motors. Sideband components are induced in stator current if BRB fault is present and commonly used for diagnosis purposes. Filippetti criterion has been proposed and developed in the literature to quantify the severity of BRB fault by using sideband components, however, with unsatisfactory universality/accuracy. This paper briefly analyzes Filippetti criterion, and mathematically derives a new criterion for the quantification of BRBs, based on the electromagnetic mechanism of sideband components. Comparing to Filippetti criterion, the universality and accuracy of the new criterion are both improved. Moreover, the new criterion includes the influence from the interbar currents associated with BRB fault. The related experiment on a 3-kW, 380-V, 50-Hz induction motor has been conducted, and results show the superiority of the new criterion over Filippetti criterion.

Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Artificial Neural Networks

We present an algorithm for the online detection of rotor bar breakage in induction motors through the use of wavelet packet decomposition (WPD) and neural networks. The system provides a feature representation of multiple frequency resolutions for faulty modes and accurately differentiates between healthy and faulty conditions, and its main applicability is to dynamic time-variant signals experienced in induction motors during run time. The algorithm analyzes rotor bar faults by WPD of the induction motor stator current. The extracted features with different frequency resolutions, together with the slip speed, are then used by a neural network trained for the detection of faults. The experimental results show that the proposed method is able to detect the faulty conditions with high accuracy.

Novel Methodology for Online Half-Broken-Bar Detection on Induction Motors

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but is also how early these failures can be detected. Squirrel-cage induction motors are the most popular motors used in industry, consuming around 85% of the power in industrial plants. Broken rotor bars in induction motors are among the major failures that are desirable to detect at early stages because this failure significantly increases power consumption and is responsible for further damage to the machinery. Previously reported works base their analysis on current or vibration monitoring for broken-bar detection up to one broken bar under mechanically loaded motor conditions. The contribution of this paper presents a novel methodology for half-broken-bar detection, which combines current and vibration analysis by correlating the signal spectra to enhance detectability for mechanically loaded and unloaded operating conditions of the motor, which the other isolated techniques are unable to detect. The proposed methodology is implemented in a low-cost field-programmable gate array (FPGA), giving a special-purpose system-on-a-chip (SoC) solution for online operation, with the development of a complex postprocessing decision-making unit. Several cases of study are presented to demonstrate the performance of the implementation.

Criterion Function for Broken-Bar Fault Diagnosis in Induction Motor under Load Variation Using Wavelet Transform

In this article, a novel criterion function is introduced to diagnose the breakage in rotor bars of induction motors. This criterion function facilitates the precise diagnosis of the fault in induction motors under load variation. It uses wavelet transform to process the stator current signal in the faulty induction motors to extract the wavelet coefficients in a specific frequency band. Furthermore, spectrum analysis of the stator current around the fundamental frequency current is used to diagnose the fault non-invasively. It is shown that the amplitudes of the frequency harmonics components (1 ± 2ks)f s are increased due to the load increases. A time-stepping finite element method is used for modeling the faults in induction motors. In this modeling, the effects of the spatial distribution of the stator winding, nonuniform air-gap permeance, geometrical and physical characteristics of different parts of the motor, and, finally, nonlinearity of the core materials are taken into account. The proposed algorithm is applied to the stator current of a healthy and a faulty induction motor. The simulation results are obtained, and their accuracy is verified by the experimental results.

A New Pattern for Detecting Broken Rotor Bars in Induction Motors During Start-Up

This paper introduces a novel frequency pattern for diagnosis of broken rotor bars in induction motors during the start-up period. This is important in the case of start-up of large induction motors with long starting time and also motors with frequent start-up. The amplitude of the side-band components of the introduced frequency pattern is a suitable index for diagnosing the broken bars and determining the number of faulty bars precisely during start-up. The paper investigates the performance of a faulty motor. Because the number of broken rotor bars and the load are two major parameters affecting the performance of a faulty motor, the frequency spectrum of torque and speed in a motor with different numbers of broken rotor bars and different loads is analyzed. The time-stepping finite-element method (TSFEM) is used to model the motor. This model takes into account the spatial distribution of the stator windings, nonuniform magnetic permeance of the air gap, nonlinear characteristic of the cores, and continuous rotation of the rotor. The paper compares simulation results in steady-state mode with experimental results and shows that the results agree well.

Broken rotor bars detection by a new non-invasive diagnostic procedure

A new technique of diagnosing data for broken rotor bars in induction motors derived from two of the three stator currents, the Beirut diagnostic procedure (BDP) is presented in this paper. The theoretical principles directly related to the application of this diagnostic technique are described, emphasizing the use of a severity factor in order to evaluate the extension of the fault. Defining the severity factor as the normalized amplitude of the fault characteristic frequency enables us to draw up a table of comparison of several usual electric diagnostic methods. Besides the traditional one-phase current spectrum analysis, values of the severity factor related to electrical signals like the instantaneous powers, the current space vector modulus and finally related to the new Beirut diagnostic method are analyzed with respect to the variation of the power factor angle and of the sum of the two current side-band angular displacement. The BDP offers several advantages over the usual motor current signature analyses (MCSA) methods: it is shown how the proposed severity factor applied to the new diagnostic technique is not dependent on motor parameters such as the power factor angle and the fault type which is not the case of the instantaneous powers. In addition, the BDP has the advantage of detecting easily fault characteristic frequencies, which is not possible via diagnostic methods that use the detection of two side-band components as in the simple current spectrum. By theoretical analysis, computer simulations, and laboratory experiments, it is shown that the new method enhances the reliability of diagnostics of broken rotor bars in induction motor.

Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors

Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor-current spectrum. It has been shown that these broken-rotor-bar specific frequencies are located around the fundamental stator current frequency and are termed lower and upper sideband components. Broken-rotor-bar fault-detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple discriminant analysis (MDA) and artificial neural networks (ANNs) provide appropriate environments to develop such fault-detection schemes because of their multiinput-processing capabilities. This paper describes two fault-detection schemes for a broken-rotor-bar fault detection with a multiple signature processing and demonstrates that the multiple signature processing is more efficient than a single signature processing. The first which will be named the scheme, is based on a single large-scale MDA or ANN unit representing the complete operating load-torque region of the motor, while the second which will be named the partition scheme, consists of many small-scale MDA or ANN units, each unit representing a particular load-torque operating region. Fault-detection performance comparison between the MDA and the ANN with respect to the two schemes is investigated using the experimental data collected for a healthy and a broken-rotor-bar case. Partition scheme distributes the computational load and complexity of the large-scale single units in a monolith scheme to many smaller units, which results in the increase of the broken-rotor-bar fault-detection performance, as is confirmed with the experimental results

Fault detection of broken rotor bars in induction motor using a global fault index

Induction motors play a very important part in the safe and efficient running of any industrial plant. Early detection of abnormalities in the motor would help to avoid costly breakdowns. Accordingly, this work presents a technique for the diagnosis of broken rotor bars in induction motor. Stator voltage and current in an induction motor were measured and employed for computation of the input power of one stator phase. Waveforms of the instantaneous power and line current were subsequently analyzed using the Bartlett periodogram. Different global fault indexes on the instantaneous power spectrum and on the line current spectrum for the fault detection are evaluated. Several rotor cage faults of increasing severity were studied with various load effects. Experimental results prove the efficiency of the employed method.