Stator Current Monitoring Research Articles

Detection of simultaneous mechanical faults in 6‐kV pumping induction motors using combined MCSA and stray flux methods

The popularity of stray and air-gap flux monitoring methods is increasing. This trend is justified by several advantages of such methods over the stator current monitoring that has been demonstrated for electrical fault detection in the induction and synchronous machines. However, the use of the magnetic flux for mechanical faults detections has not drawn this much attention, while in industry, the vibration analysis continues to be popular. This study comes to bridge this gap via the detection of mechanical faults of 6-kV induction motors in a pumping station. The diagnostic procedure mainly involves the stator current and stray flux monitoring, and harmonic index analysis. The localisation of the fault has been made possible via oscillometer readings. It will be shown that mechanical faults have a very different impact on the stator current and the flux signals, while the flux is not sensitive to the bearing fault mechanisms.

Experimental detection of defects in variable speed fan bearing using stator current monitoring

This article presents an experimental contribution to detect and diagnose mechanical faults, including bearing faults, which are the cause of most faults in single or variable speed fan motor systems. For this, we proceed to the extraction of the characteristics appearing in the supply current signal of the diagnostic system, by the application of three complementary approaches. During our experimental study, we found that one of the best methods for diagnosing single or multiple mechanical faults in induction motor systems is MCSA (Motors Current Signature Analysis), to which we applied the FFT (Fast Fourier transform) spectral analysis completed by the park vector approach orbital (OPVA) applied to the components of the current stator vector. In order to confirm the reliability of our diagnosis, these two approaches have been supplemented by the Gabor spectrogram (STFT). The practical validation of this plural methodology, in the case of a bearing degradation, was carried out on a variable speed fan motor of 0.25 HP. The results obtained by this fault detection technique are satisfactory and interesting because they make it possible to anticipate breakdowns and, therefore, to program and control the maintenance costs of industrial systems without affecting production.

Bearing Fault Detection in Induction Machine Based on Stator Current Spectrum Monitoring

Bearing Fault Detection in Induction Machine Based on Stator Current Spectrum Monitoring

Detection of Bearing Faults in Mechanical Systems Using Stator Current Monitoring

Induction motors have been responsible for running mechanical systems in the industry for many decades. Their diagnosis still remains a hot quest for the researchers using various techniques. In this study, motor current signature analysis (MCSA) technique has been used to detect the faulty bearing installed in load machine (coupled to an induction motor). It has been seen that faulty bearings installed in load machines do not directly alter airgap eccentricity of an induction motor. In fact, these bearing faults affect the resultant torque of an induction motor. As modulating fault components show very low amplitude, these are usually masked by noise. This paper is devoted towards extracting features of faulty components efficiently from stator current using continuous wavelet transform. This methodology is assessed for detecting outer race faults in bearings installed in load machines using MCSA.

固定子電流パワースペクトルの特徴周波数監視による電動機軸受の異常検出技術

固定子電流パワースペクトルの特徴周波数監視による電動機軸受の異常検出技術

A Novel Improved ELM Algorithm for a Real Industrial Application

It is well known that the feedforward neural networks meet numbers of difficulties in the applications because of its slow learning speed. The extreme learning machine (ELM) is a new single hidden layer feedforward neural network method aiming at improving the training speed. Nowadays ELM algorithm has received wide application with its good generalization performance under fast learning speed. However, there are still several problems needed to be solved in ELM. In this paper, a new improved ELM algorithm named R-ELM is proposed to handle the multicollinear problem appearing in calculation of the ELM algorithm. The proposed algorithm is employed in bearing fault detection using stator current monitoring. Simulative results show that R-ELM algorithm has better stability and generalization performance compared with the original ELM and the other neural network methods.

Time-Domain Stator Current Condition Monitoring: Analyzing Point Failures Detection by Kolmogorov-Smirnov (K-S) Test

Time-Domain Stator Current Condition Monitoring: Analyzing Point Failures Detection by Kolmogorov-Smirnov (K-S) Test

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.

Bearing Fault Detection via Stator Current Noise Cancellation and Statistical Control

This paper proposes a new approach to detect <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situ</i> bearing faults via stator current monitoring. For <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situ</i> bearing faults, the characteristic bearing fault frequencies may not exist, particularly at an early stage. In addition, the bearing fault signatures are usually subtle compared to the dominant components in the sampled stator current. Therefore, in this paper, a noise cancellation technique is used to suppress those dominant components that are not related to a potential bearing fault. The remaining components, i.e., the noise-cancelled stator current, are then closely related to the health condition of the bearing. Furthermore, it is observed that under the presence of a bearing fault, the noise-cancelled stator current displays a significant amount of degrees of uncontrolled variation in its magnitude. The uncontrolled variation is detected by observing the samples falling outside the three-sigma limits on Shewhart's control charts. Therefore, it is possible to detect <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situ</i> bearing faults by detecting the variation in magnitude of the noise-cancelled stator current, as verified by online experiments performed in this paper.

Hilbert versus Concordia transform for three-phase machine stator current time-frequency monitoring

This paper deals with mechanical fault diagnosis in three-phase induction machines from stator current measurements. According to machine models, mechanical faults lead to amplitude and/or phase modulations of the measured stator current with possibly time varying carrier frequency. The modulation diagnosis requires a univocal definition of the instantaneous phase and amplitude. This is performed by associating a complex signal to the real measured one. For a convenient separate modulation diagnosis, the complex signal instantaneous phase and amplitude are expected to carry, respectively, information about the phase and amplitude modulations. The complex signal is classically obtained through the Hilbert transform. Under Bedrosian conditions, the so-called analytic signal allows a separate modulation diagnosis. However, mechanical faults may also produce fast modulations violating the Bedrosian conditions. This study proposes an alternative complex signal representation which takes advantage of the three stator current measurements available in a three-phase machine. From two stator current measurements, the Concordia transform builds a complex vector, the so-called space vector, which unconditionally allows separate modulation diagnosis. This paper applies and compares the Hilbert and Concordia transforms, theoretically and in case of simulated and experimental signals with various modulation frequency ranges.

Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control

This paper proposes a new approach to detect in situ bearing faults via stator current monitoring. For in situ bearing faults, the characteristic bearing fault frequencies may not exist, particularly at an early stage. In addition, the bearing fault signatures are usually subtle compared to the dominant components in the sampled stator current. Therefore, in this paper, a noise cancellation technique is used to suppress those dominant components that are not related to a potential bearing fault. The remaining components, i.e., the noise-cancelled stator current, are then closely related to the health condition of the bearing. Furthermore, it is observed that under the presence of a bearing fault, the noise-cancelled stator current displays a significant amount of degrees of uncontrolled variation in its magnitude. The uncontrolled variation is detected by observing the samples falling outside the three-sigma limits on Shewhart's control charts. Therefore, it is possible to detect in situ bearing faults by detecting the variation in magnitude of the noise-cancelled stator current, as verified by online experiments performed in this paper.

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

Mechanical Fault Detection in a Medium-Sized Induction Motor Using Stator Current Monitoring

This paper presents the results of an experimental study of the detection of mechanical faults in an induction motor. As is reasonably well known, by means of analysis of combinations of permeance and magneto-motive force (MMF) harmonics, it is possible to predict the frequency of air gap flux density harmonics which occur as a result of certain irregularities in an induction motor. In turn, analysis of flux density harmonics allows the prediction of induced voltages and currents in the stator windings. Reviewing this theory, equations which may aid in the identification of mechanical faults are presented. These equations include both those which indicate eccentric conditions and those which have been suggested to help identify bearing faults. The development of test facility to create eccentricity faults and bearing fault conditions is described. This test facility allows rapid access to the motor bearings, allowing an investigation into the ability to detect faulted bearing conditions using stator current monitoring. Experimental test results are presented, indicating that it may be possible to detect bearing degradation using relatively simple and inexpensive equipment.

A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings

Rolling element bearings are critical components in induction motors and monitoring their condition is important to avoid failures. Several condition monitoring techniques for the bearings are available. Out of these stator current monitoring is a relatively new technique. Vibration, stator current, acoustic emission and shock pulse methods (SPMs) for the detection of a defect in the outer race of induction motor ball bearing have been compared. The measurements were performed at different loads. The defect in the bearing could be detected by all the methods. Acoustic emission monitoring proved to be the best method followed by SPM when the increase in the levels of the measured parameters were compared with respect to those of healthy bearings.

A current monitoring system for diagnosing electrical failures in induction motors

Induction motors are critical components in industrial processes. A motor failure may yield an unexpected interruption at the industrial plant, with consequences in costs, product quality, and safety. Many of these faulty situations in three phase induction motors have an electrical reason. Among different detection approaches proposed in the literature, those based on stator current monitoring are advantageous due to its non-invasive properties. One of these techniques resorts to spectrum analysis of machine line current. Another non-invasive technique is the Extended Park's Vector Approach, which allows the detection of inter-turn short circuits in the stator winding. This article presents the development of an on-line current monitoring system that uses both techniques for fault detection and diagnosis in the stator and in the rotor. Based on experimental observations and on the knowledge of the electrical machine, a knowledge-based system was constructed in order to carry out the diagnosis task from these estimated data.

Induction Motor Failure Detection Using Geometric Signal Separation

We present a new method for on-line induction motor failure detection using stator current monitoring. The concept of geometric signal separation in feature space is introduced. A set of observations from a single phase of the stator current is transformed into a feature vector. After establishing a local metric in the feature space, close neighbors of the present vector are searched for in a database representing allowed states of the motor, which were recorded during a training period. In their absence the present state is a novelty, which is considered as a failure if it persists for a certain time. This monitoring scheme successfully deals with varying load conditions of the motor, where also oscillating load torques can be tackled. Additionally, observations from a healthy motor, which differ considerably from any known allowed state in the database due to changes of environmental conditions can be distinguished from true failures at a significant level. We do not use expert rules or detailed knowledge about the monitored motor.

Motor bearing damage detection using stator current monitoring

This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. Vibration monitoring of mechanical bearing frequencies is currently used to detect the presence of a fault condition. Since these mechanical vibrations are associated with variations in the physical air gap of the machine, the air gap flux density is modulated and stator currents are generated at predictable frequencies related to the electrical supply and vibrational frequencies. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. The bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. Experimental results which show the vibration and current spectra of an induction machine with different bearing faults are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault.

An unsupervised, on-line system for induction motor fault detection using stator current monitoring

An unsupervised, on-line system for induction motor fault detection using stator current monitoring