A novel detection method of motor broken rotor bars based on wavelet ridge

Detection of cage motor broken rotor bars has long been an important but difficult job in the detection area of motor faults. The characteristic frequency component of faulted rotor (CFCFR) is very close to the power frequency component but by far less in amplitude, which brings about great difficulty for accurate detection. A new detection method based on wavelet ridge is presented in this paper. Aiming at the motor's starting period during which the motor accelerates progressively and CFCFR approaches the power frequency gradually in frequency spectrum, the wavelet ridge-based method is adopted to analyze this transient procedure and the CFCFR is extracted. The influence of power frequency can be effectively eliminated, and detection accuracy can be greatly improved by using the approach presented in this paper. Also, this is indeed a novel but excellent approach for the detection domain of cage induction motor broken rotor bars.     

Detection of broken bars in induction motors using an extendedKalman filter for rotor resistance sensorless estimation

This paper deals with broken bars detection in induction motors. The hypothesis on which detection is based is that the apparent rotor resistance of an induction motor will increase when a rotor bar breaks. To detect broken bars, measurements of stator voltages and currents are processed by an extended Kalman filter for the speed and rotor resistance simultaneous estimation. In particular, rotor resistance is estimated and compared with its nominal value to detect broken bars. In the proposed extended Kalman filter approach, the state covariance matrix is adequacy weighted leading to a better states estimation dynamic. Its main advantage is the correct rotor resistance estimation even for an unloaded induction motor. As part of this estimation process, it is necessary to compensate for the thermal variation in the rotor resistance. Computer simulations, carried out for a 4 kW four-pole squirrel cage induction motor, provide an encouraging validation of the proposed sensorless broken bars detection technique     

Asynchronous Machine Rotor Fault Diagnosis Technique Using Complex Wavelets

This paper introduces a novel approach for the detection of rotor faults in asynchronous machines, based on wavelet analysis of the stator phase current. To be more specific, the measured stator phase current is filtered through a complex wavelet. Theoretical analysis validates that the spectrum of the modulus of the result of the filtering is free from the fundamental supply frequency component, and the fault characteristics can be highlighted. This is advantageous, especially if the induction machine operates at low slip values, where the characteristic frequency components of the rotor fault are very close to the fundamental frequency component. At the same time, by matching the wavelet function to the frequencies of the faulty components, a narrow bandpass filter at the frequency region of the fault characteristic spectral components is obtained. Furthermore, in the context of this paper, features extracted using the proposed technique are used as input to a support vector machine classifier that is employed for the detection of the rotor fault. Simulation and experimental results demonstrate the effectiveness of the proposed technique.     

Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors

The existence of 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 settled around the fundamental stator current frequency and are termed lower and upper sideband components. Broken rotor bar fault detection schemes should depend 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) provides an appropriate environment to develop such fault detection schemes because of its multi-input processing capabilities. The focus of this paper is to provide a new fault detection methodology for broken rotor bar fault detection and diagnostics in terms of its multiple signature processing feature and the motor operation partitioning concept to improve the overall detection performance. This paper describes two fault detection schemes within this methodology, and demonstrates that multiple signature processing is more efficient than single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA unit representing the complete operating load torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA units, each unit representing a particular load torque operating region.     

A Reconfigurable Motor for Experimental Emulation of Stator Winding Interturn and Broken Bar Faults in Polyphase Induction Machines

The benefits and drawbacks of a 5-hp reconfigurable induction motor, which was designed for experimental emulation of stator winding interturn and broken rotor bar faults, are presented in this paper. It was perceived that this motor had the potential of quick and easy reconfiguration to produce the desired stator and rotor faults in a variety of different fault combinations. Hence, this motor was anticipated to make a useful test bed for evaluation of the efficacy of existing and new motor fault diagnostics techniques and not the study of insulation failure mechanisms. Accordingly, it was anticipated that this reconfigurable motor would eliminate the need to permanently destroy machine components such as stator windings or rotor bars when acquiring data from a faulty machine for fault diagnostic purposes. Experimental results under healthy and various faulty conditions are presented in this paper, including issues associated with rotor bar-end ring contact resistances that showed the drawbacks of this motor in so far as emulation of rotor bar breakages. However, emulation of stator-turn fault scenarios was successfully accomplished.      

Pattern recognition-a technique for induction machines rotor brokenbar detection

A pattern recognition technique based on Bayes minimum error classifier is developed to detect broken rotor bar faults in induction motors at the steady state. The proposed algorithm uses only stator currents as input without the need for any other variables. Initially, rotor speed is estimated from the stator currents, then appropriate features are extracted. The produced feature vector is normalized and fed to the trained classifier to see if the motor is healthy or has broken bar faults. Only the number of poles and rotor slots are needed as pre-knowledge information. A theoretical approach together with experimental results derived from a 3 hp AC induction motor show the strength of the proposed method. In order to cover many different motor load conditions, data are obtained from 10% to 130% of the rated load for both a healthy induction motor and an induction motor with a rotor having 4 broken bars     

Diagnostics of bar and end-ring connector breakage faults inpolyphase induction motors through a novel dual track of time-seriesdata mining and time-stepping coupled FE-state space modeling

This paper develops the fundamental foundations of a technique for detection of faults in induction motors that is not based on the traditional Fourier transform frequency domain approach. The technique can extensively and economically characterize and predict faults from the induction machine adjustable speed drive design data. This is done through the development of dual-track proof-of-principle studies of fault simulation and identification. These studies are performed using our proven time stepping coupled finite element-state space method to generate fault case data. Then, the fault cases are classified by their inherent characteristics, so-called "signatures" or "fingerprints." These fault signatures are extracted or mined here from the fault case data using our novel time series data mining technique. The dual-track of generating fault data and mining fault signatures was tested here on three, six, and nine broken bar and broken end-ring connectors in a 208-volt, 60-Hz, 4-pole, 1.2-hp, squirrel cage 3-phase induction motor     

What stator current processing-based technique to use for induction motor rotor faults diagnosis?

In recent years, marked improvement has been achieved in the design and manufacture of stator winding. However, motors driven by solid-state inverters undergo severe voltage stresses due to rapid switch-on and switch-off of semiconductor switches. Also, induction motors are required to operate in highly corrosive and dusty environments. Requirements such as these have spurred the development of vastly improved insulation material and treatment processes. But cage rotor design has undergone little change. As a result, rotor failures now account for a larger percentage of total induction motor failures. Broken cage bars and bearing deterioration are now the main cause of rotor failures. Moreover, with advances in digital technology over the last years, adequate data processing capability is now available on cost-effective hardware platforms, to monitor motors for a variety of abnormalities on a real time basis in addition to the normal motor protection functions. Such multifunction monitors are now starting to displace the multiplicity of electromechanical devices commonly applied for many years. For such reasons, this paper is devoted to a comparison of signal processing-based techniques for the detection of broken bars and bearing deterioration in induction motors. Features of these techniques which are relevant to fault detection are presented. These features are then analyzed and compared to deduce the most appropriate technique for induction motor rotor fault detection.     

A novel method to detect broken rotor bars in squirrel cage induction motors when interbar currents are present

The paper details the theoretical and experimental investigation into the axial force produced by broken rotor bars on copper cage rotors. It introduces interbar current theory and the overall fault progression of broken rotor bars. The paper also presents a mathematical model showing that interbar currents interact with stator flux to produce a force in the axial direction. To substantiate the model, the paper provides experimental results on several motors having broken rotor bars. The experimental results verify the frequencies postulated in the mathematical model. The measurements also show that these components are load dependent and due to interbar currents in the rotor. The experimental results suggest a new method to detect for broken rotor bars based on the presence of interbar currents. This method may be used to aid the existing methods used today.     

Multiple Reference Frames Theory: A New Method for the Diagnosis of Stator Faults in Three-Phase Induction Motors

This paper proposes the use of the multiple reference frames theory for the diagnosis of stator faults in three-phase induction motors. The development of a simplified mathematical motor model allowed the establishment of the equivalent circuits of the motor, in$d!!-!!q!!-!!0$axes, in the presence of stator interturn short circuits. The use of the stationary reference frame, clockwise and counterclockwise synchronous reference frames, allows the extraction and manipulation of the information contained in the motor supply currents in a way that the effects introduced by the fault are easily isolated and measured. A severity factor is defined and the simulation and experimental results presented demonstrate its independence in relation to the working conditions of the motor, such as the load level and unbalances in the voltage supply system. Although the technique is here introduced for the diagnosis of stator faults, it is possible to extend its use for the diagnosis of other asymmetries such as broken rotor bars and air-gap eccentricity.     

Determination of interbar current effects in the detection ofbroken rotor bars in squirrel cage induction motors

In present monitoring techniques for the detection of broken rotor bars in squirrel cage induction motors, the assumption is made that a broken bar conducts no current. This causes a magnetic imbalance, which can then be detected to indicate a broken bar. In certain motors, however, large currents are still able to flow in the broken bar by means of interbar currents. This paper deals with the effects these currents have on broken bar monitoring. The work shows theoretically and experimentally that interbar currents reduce any magnetic imbalance brought about by the broken bar     

Effects of broken bars/end-ring connectors and airgapeccentricities on ohmic and core losses of induction motors in ASDsusing a coupled finite element-state space method

In this paper, effects of rotor abnormalities such as broken squirrel-cage bars, broken cage connectors and airgap eccentricity on ohmic and core losses of induction motors are presented. In this investigation, a comprehensive time-stepping coupled finite element-state space (TSCFE-SS) model was fully utilized to compute the time-domain elemental flux density waveforms and various time-domain waveforms of motor winding currents useful for core loss and ohmic loss computations. Such investigation is feasible by use of the TSCFE-SS model due to its intrinsic nature and characteristics. The results obtained from the simulations of an example 1.2-hp induction motor clearly indicate that faults due to broken squirrel-cage bars/end-connectors can increase motor core losses in comparison to the healthy case. The results also give the effect of saturation on the core loss distributions within the cross-section of the motor, and indicate the potential for possible excessive loss concentrations and consequent hot spots near zones of bar and connector breakages in the rotor     

Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip

This paper proposes an online/offline induction motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms, based on the Hilbert transform. MCSA is a method for motor diagnosis with stator-current signals. Although it is one of the most powerful online methods for diagnosing motor faults, it has some drawbacks that can degrade the performance and accuracy of a motor-diagnosis system. In particular, it is very difficult to detect broken rotor bars when the motor is operating at low slip or under no load, due to fast Fourier transform (FFT) frequency leakage and the small amplitude of the current components related to the fault. Therefore, advanced signal-and-data-processing algorithms are proposed. They consist of a proper sample selection algorithm, a Hilbert transformation of the stator-sampled current, and spectral analysis via FFT of the modulus of the resultant time-dependent vector modulus for achieving MCSA efficiently. Experimental results obtained on a 1.1-kW three-phase squirrel-cage induction motor are discussed.      

Broken rotor bar detection in induction machines with transient operating speeds

Previous work on condition monitoring of induction machines has focused on steady-state speed operation. Here, a new concept is introduced based on an analysis of transient machine currents. The technique centers around the extraction and removal of the fundamental component of the current and analyzing the residual current using wavelets. Test results of induction machines operating both as a motor and a generator shows the ability of the algorithm to detect broken rotor bars.     

Assessment of interbar currents in double-cage induction motorswith broken bars

Research has shown that it is possible for currents to flow in a broken rotor bar of a squirrel cage induction motor by means of interbar currents. Such research dealt only with single-cage motors. Further work was necessary when considering double-cage motors. This paper proves theoretically and experimentally that interbar currents also exist in double-cage motors. The influence of interbar currents on the current distribution around the broken bar is also shown     

Online cage rotor fault detection using air-gap torque spectra

This paper discusses use of air-gap torque spectra as a means of identifying faults in cage rotors. Being dependent on both stator and rotor currents, the torque is very sensitive to faults in the rotor. Through a comparative study using a detailed machine model and the standard dq model, the paper shows that the characteristic frequencies generated by a particular fault are preserved even if the standard dq model is used for estimation of air-gap torque. This is validated through a practical hardware implementation for online spectrum estimation of air-gap torque using TMS320C31, where several faulted cage rotors were used for study.     

Induction motors variable speed drives diagnosis through rotor resistance monitoring

Induction motor driven by vector control method makes high performance control of torque and speed possible. The decoupling of flux and electromagnetic torque obtained by field orientation depends on the precision and the accuracy of the estimated states. Rotor asymmetries lead to perturbations of air gap flux patterns in induction machines. These perturbations in flux components affect the electromagnetic torque, as well as stator currents and voltages. This paper first investigates the control of the induction motor using an extended Kalman filter (EKF) for a direct field-oriented control. It then studies the broken rotor bars (BRBs) fault by the monitoring the rotor resistance. The hypothesis on which the detection is based is that the apparent rotor resistance of the motor will increase when a rotor bar breaks. The rotor resistance is estimated and compared with its nominal value to detect BRBs fault. The EKF estimates the rotor flux, speed and rotor resistance on line by using only measurements of the stator voltages and currents. Simulation results show the effectiveness of the proposed method in the cases of load torque perturbation and speed reversion.     

Comparison between characterization and diagnosis of brokenbars/end-ring connectors and airgap eccentricities of induction motorsin ASDs using a coupled finite element-state space method

This paper describes how a rigorous and comprehensive time-stepping coupled finite element-state space (TSCPE-SS) modeling technique can be utilized in diagnostics and differentiation between induction motor rotor (cage) abnormalities of broken bars/connectors and airgap eccentricities. The model is used for the computation of time-domain performance characteristics, such as the stator phase current waveforms and developed torque profiles including these abnormalities. This is followed by analysis of the current waveforms and torque profiles using fast Fourier transform to obtain their corresponding frequency spectra. Comparison between the TSCFE-SS model's simulation results, which correlate very well with theoretical results, clearly illustrate that rotor bar and/or end-ring connector breakages can be distinguished from static and dynamic airgap eccentricities. This paper also gives an interesting comparison between the effects and implications of these various rotor abnormalities on machine parameters and performance characteristics. Furthermore, the results indicate that frequency components reported earlier to be produced only by the combined effects of static and dynamic airgap eccentricity could be observed in case of either static or dynamic eccentricity. Finally, this paper demonstrates the possible opportunities that can be made use of in noninvasive detection of airgap eccentricities via TSCFE-SS and current signature techniques     

A combined finite element-state space modeling environment forinduction motors in the ABC frame of reference: the blocked-rotor andsinusoidally energized load conditions

The combined finite element-state space (CFE-SS) modeling environment was used to predict the performance of a 1.2 hp, three-phase case-study squirrel cage induction motor under blocked rotor and typical load operating conditions. The nature of this CFE-SS environment allows one to rigorously account for the impact of space harmonics generated by the magnetic circuit, winding, and cage geometric, as well as layout peculiarities and magnetic saturation, on the current and torque profiles, and ohmic losses in the stator armature and cage. This includes the ability to predict the profiles of connector and bar currents. The results of the CFE-SS simulations compare favorably with blocked rotor and load experimental test data. Potential capabilities of this CFE-SS modeling environment, and its use in impacting motor design decisions, are discussed in the light of reported findings     

Determination of radial-forces in relation to noise and vibrationproblems of squirrel-cage induction motors

The radial electromagnetic forces in induction motors play an important role in the production of audible noise and vibrations. The magnetic flux pulsations at the iron surfaces produce these radial forces, which act on the stator and rotor structures. An analysis for the calculation of the various field harmonics and radial forces in squirrel cage induction motors is presented in this paper. To verify the validity of the analysis, a squirrel cage induction motor is analyzed. Theoretical and experimental results are presented with a view to determine the actual role played by the air-gap harmonic fields on the radial forces. Also, the effects of loading on the radial forces and the ensuing vibrations are closely examined