Amplitude Of Sideband Components Research Articles

Diagnosis and detection of dynamic eccentricity fault for permanent magnet transverse flux generator

The authors analyse no-load and full-load performance of a permanent magnet transverse flux generators (PMTFGs) in fully aligned condition as well as under different types of mechanical faults including static eccentricity (SE), dynamic eccentricity (DE), inclined rotor (IR) and run-out (RO) faults. An analytical model is developed to calculate the air-gap permeance. This permeance is used to estimate the flux density and back-electromotive force in the healthy and faulty machine. Performance of the machine is predicted using the proposed analytical model and verified by comparing it with the results obtained using three-dimensional time stepping finite element method. Finally, the spectra of stator current in the PMTFG under SE, DE, IR and OR faults are determined through which the amplitude of side-band components with a specific frequency pattern is extracted. It can detect the eccentricity fault precisely, recognise its type and determines its severity.

A Novel Approach for Detection of Shorted Turns Fault in Machine Using Combination of Flux and Instantaneous Power Signal

In this paper, non-invasive multiple sensors for the detection of shorted turns fault have been used. The fault has been detected using fault frequency components from flux and instantaneous power and a single signal (combination of flux and instantaneous power signals) called power-flux over a wide range of loads. To achieve this task, stator winding of the machine was re-wounded with taps to introduce shorted turns fault. An extensive series of laboratory tests were conducted to examine the fault frequency amplitudes for the healthy motor and faulty motors with different shorted turns. Results based on the variations in the amplitude of sideband components between healthy and faulty motors versus different frequencies over a wide range of loading conditions from different signals have been analyzed experimentally in detail. Overall, this paper provides comprehensive detail to detect the shorted turn faults in machines using a novel combination of axial flux and instantaneous power signals into a single signal called power-flux.

Detection of Eccentricity Faults in Machine UsingFrequency Spectrum Technique

This paper investigated the current, flux and instantaneous power signals as a diagnostic medium to detect the eccentricity faults under varying load conditions using eccentricity fault frequency components. The analysis of results based on the variations in the amplitude of sideband components between healthy and faulty motors versus average eccentricity level over a wide range of loading conditions from different signals. This study examine the usefulness of instantaneous power to detect the eccentricity faults under varying loading conditions and also shows the prime features of different signals to detect the eccentricity faults using the variations between the healthy and faulty motors when the level of average eccentricity is high and the rated load is light. Overall, this paper provides comprehensive detail to detect the eccentricity faults using current, axial flux and instantaneous power signals simultaneously.

Feature Extraction for Short-Circuit Fault Detection in Permanent-Magnet Synchronous Motors Using Stator-Current Monitoring

In this paper, a novel frequency pattern and competent criterion are introduced for short-circuit-fault recognition in permanent-magnet synchronous motors (PMSMs). The frequency pattern is extracted from the monitored stator current analytically and the amplitude of sideband components at these frequencies is introduced as a proper criterion to determine the number of short-circuited turns. Impacts of the load variation on the proposed criterion are investigated in the faulty PMSM. In order to demonstrate the aptitude of the proposed criterion for precise short-circuit fault detection, the relation between the nominated criterion and the number of short-circuited turns is specified by the mutual information index. Therefore, a white Gaussian noise is added to the simulated stator current and robustness of the criterion is analyzed with respect to the noise variance. The occurrence and the number of short-circuited turns are predicted using support-vector machine as a classifier. The classification results indicate that the introduced criterion can detect the short-circuit fault incisively. Simulation results are verified by the experimental results.

Diagnosis and performance analysis of three-phase permanent magnet synchronous motors with static, dynamic and mixed eccentricity

Albeit permanent magnet synchronous motors (PMSMs) under static and dynamic eccentricity have been studied in few papers, so far mixed eccentricity has not been investigated. In this study, a novel index is introduced for dynamic and mixed eccentricity fault diagnosis in PMSM. This index is the amplitude of side-band components with a particular frequency pattern that is extracted from spectrum analysis of the motor torque. The index provides precise detection of the eccentricity occurrence, recognition of its type and determination of its degree. To evaluate the ability of the proposed index for the eccentricity diagnosis and estimation of its severity, first the correlation between indices, static and dynamic eccentricity degrees are calculated. The type of eccentricity is determined using k-nearest neighbour (KNN) classifier. At the next step, a three-layer artificial neural network is employed to estimate the eccentricity degree of torque profiles based on their type of eccentricity. After all, a white Gaussian noise is added to the simulated torque and robustness of the proposed index is analysed with respect to the noise variance. Meanwhile, the spectrum analysis of the stator current in faulty PMSM under static, dynamic and mixed eccentricities is studied and the influence of these eccentricities on ohmic, hysteresis and eddy current losses of PMSM are presented. In this examination, the time-domain elemental flux density waveforms and various time-domain waveforms of motor winding currents are computed using time stepping finite element method (TSFEM) for core loss and ohmic loss calculations punctually. The accuracy of obtained simulation results is verified using experimental results.

Pattern identification for eccentricity fault diagnosis in permanent magnet synchronous motors using stator current monitoring

A novel theoretical scrutiny is presented here, which extracts eccentricity fault signatures by monitoring the stator current in permanent magnet synchronous motors (PMSMs). In this analysis, effects of the stator slots and saturation are taken into account for static and dynamic eccentricity fault detection. Eccentricity signatures are utilised to introduce a particular frequency pattern, and amplitude of side-band components at proposed frequencies is employed as a proper index for eccentricity fault recognition. Competence of the nominated index to detect eccentricity, its type and its degree is investigated in faulty PMSM with different load levels. Hence, time-stepping finite-element method is used to model faulty PMSM and calculate stator currents for processing and obtaining aforementioned proposed index. The relation between the nominated index, static and dynamic eccentricity degrees is determined by a mutual information criterion. So, a white Gaussian noise is added to the simulated current and robustness of the proposed index is analysed with respect to the noise variance. Finally, the type and the degree of eccentricity are predicted using support vector machine as a classifier. The classification results indicate that the proposed features can estimate the eccentricity degree and its type. The simulation results are verified by the experimental results.

Static-, Dynamic-, and Mixed-Eccentricity Fault Diagnoses in Permanent-Magnet Synchronous Motors

Mixed-eccentricity (ME) fault diagnosis has not been so far documented for permanent-magnet (PM) synchronous motors (PMSMs). This paper investigates how the static eccentricity (SE), dynamic eccentricity (DE), and ME in three-phase PMSMs can be detected. A novel index for noninvasive diagnosis of these eccentricities is introduced for a faulty PMSM. The nominated index is the amplitude of sideband components with a particular frequency pattern which is extracted from the spectrum of stator current. Using this index makes it possible to determine the occurrence, as well as the type and percentage, of eccentricity precisely. Meanwhile, the current spectrum of the faulty PMSM during a large span is inspected, and the ability of the proposed index is exhibited to detect eccentricity in faulty PMSMs with different loads. A novel theoretical scrutiny based on a magnetic field analysis is presented to prove the introduced index and generalize the illustrated fault recognition method. To show the merit of this index in the eccentricity detection and estimation of its severity, first, the correlation between the index and the SE and DE degrees is determined. Then, the type of the eccentricity is determined by a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor classifier. At the next step, a three-layer artificial neural network is employed to estimate the eccentricity degree and its type. After all, a white Gaussian noise is added to the simulated current, and the robustness of the proposed index is analyzed with respect to the noise variance. In this paper, the PMSM under magnetic fault (demagnetization) and electrical faults (short and open circuits) is modeled, and the current spectrum of the faulty PMSM under demagnetization, short circuit, and open circuit faults is analyzed. It is demonstrated that the proposed index, due to eccentricity fault, is not generated in the current spectrum due to magnetic and electrical faults. Indeed, it is exposed that the introduced index is only created due to eccentricity fault and it is not sensitive to other faults. To model the PMSM eccentricities, a time-stepping finite-element method, which takes into account all geometrical and physical characteristics of the machine components, nonuniform permeance of the air gap, and nonuniform characteristics of the PM material, is employed. This model facilitates the access to the demanded signals in order to have accurate processing. A comparison of simulation and experimental results validate the proposed index.