Motor Current Signature Analysis for the Air Gap Eccentricity Detection In the Squirrel Cage Induction Machines

Motor current signature analysis (MCSA) techniques measure slip-related current signals in the supply lines of a squirrel-cage induction machine (SCIM) for slip estimation, fault detection, and motor diagnostics. In a traditional setup for MCSA, current sensors directly measure the machine currents and are usually installed physically close to the machine of interest, e.g., current transducers clamped about the machine stator leads. For grid-connected SCIMs, the slip-related current signals propagate through the power system. Thus, a single aggregate power monitor can potentially perform MCSA for a collection of SCIMs powered by a common electrical service, referred to in this article as nonintrusive MCSA. Current division and system impedances (source impedance, load impedances, and machine parameters) determine the applicability of nonintrusive MCSA. This article demonstrates measurement hardware, modeling, and experimental results for nonintrusive MCSA. Analysis and results investigate the current-source model of a grid-connected SCIM and its interaction with the system impedances. Applied to a distribution system with multiple machines, the techniques inform when nonintrusive MCSA is possible for collections of SCIMs and equivalent to traditional MCSA.

The authors present the faults diagnosis by parameter identification of the squirrel-cage rotor induction machine using real data. The model of electric parameter identification of the induction machine from the input-output observations of the stator is elaborated.To experimentally verify this approach, the tests are carried out on four squirrel-cage rotor induction machines especially constructed for the purpose of the diagnosis. All the model parameters of the squirrel-cage rotor induction machine are identified by least-squares method. Experimental results show good agreement and confirm the possibility the detection and localization of the faults.

This paper presents the mechanical fault detection in squirrel cage induction motors (SCIMs). Rotor eccentricity is a common fault in SCIMs. In addition, rolling element bearing (REB) faults is a major problem among different faults, which cause catastrophic damage to rotating electrical machines. This fault is systematically, resulting in undesirable vibrations in several machines. Generally, the REB faults create a static, dynamic and mixed eccentricity type. The questions that arise are:- What are the logical reasons that verify the existence of mixed eccentricity, even in the new rotating electrical machines?- What is the relationship between rolling element bearing faults and the eccentricity faults?Physically, the additional harmonics due to static eccentricity and dynamic eccentricity in stator current spectra are firstly derived with the rotational direction of electromagnetic force (EMF) harmonics into consideration. Motor current signature analysis (MCSA) has become one of the most important techniques in the fault diagnosis field. MCSA has become popular for detection and Iocalization of many faults. It is generally based on fast Fourier transform FFT of the stator current in healthy and faulty state of the induction motors (IMs). This study tried to answer the questions previously asked. On the other hand, this paper has made the analysis of the bearing fault carefully. A new analytical model dedicated to bearing failure has been proposed. The conclusions drawn from the analytical and simulation results are validated experimentally. Finally, the experimental results demonstrated the high sensitivity and clarity of this technique.

Due to manufacturing tolerances and bearing ageing, rotor eccentricity is inevitable in electrical machines. The existence of rotor eccentricity in electrical machines will cause unbalanced magnetic pull (UMP), which would increase bearing friction losses. In addition, the additional magnetic flux harmonics caused by rotor eccentricity may incur additional copper and iron losses. Here, induction machines are analysed as they are more sensitive to rotor misalignment due to smaller air gap compared to other types of machines. The magnetic flux distribution and UMP for both squirrel cage and wound rotor induction machines are discussed, as they have a dissimilar rotor configuration. The existence of rotor eccentricity would increase rotor copper loss in squirrel cage induction machines and would increase iron losses in wound rotor induction machines, where the additional power losses are a function of the degree of eccentricity. Analytical models are used to estimate the additional power losses, and they were validated using finite element analysis. Finally, we investigated the impact of power losses when implementing the slip control method which is proposed in our previous literature, in order to reduce UMP.

This paper deals with the analysis of external magnetic field of three-phase induction motor in order to diagnose the air gap asymmetry caused by eccentric rotor. Dynamic eccentricity produces low frequency air gap flux components, however they can be observed in stator current only under mixed eccentricity. Unlike MCSA (motor current signature analysis), described method allows to detect purely dynamic eccentricity or to detect dynamic eccentricity under mixed eccentricity with a minimal effect of static eccentricity. Although many papers have been focused on external magnetic field analysis, they have usually decribed other types of faults. In this paper the amplitudes of characteristic frequency components are predicted using FEM and some obtained results are verified by measurement. Both purely dynamic and mixed eccentricities are taken into account as well as saturation of magnetic circuit due to its significant influence on calculated spectrum.

This paper presents a multi-position magnetic field measurement approach to detect air-gap eccentricity in induction machines, including static, dynamic and mixed eccentricity. Using this method, the position of minimum air gap of static eccentricity also can be detected. The sideband components around the fundamental frequency of magnetic flux leakage in stator yoke is particularly analyzed based on magnetic circuit. The magnitudes of induced voltage harmonics in search coils are also investigated by finite element method. The algorithm is implemented for a 4-pole three-phase cage induction machine with static, dynamic and mixed eccentricity. It is highlighted that dynamic eccentricity can be detected by any one of the search coils, static eccentricity and the position of minimum air gap can be detected by the magnitudes of induced voltages in all the search coils. The theoretical results have been validated by finite element method.

Abstract. Agricultural wide-span implement carriers (WSIC) are machines specially adapted to the controlled-traffic farming field management system. The agricultural WSIC requires many main and auxiliary drives that can be controlled separately. Electric drives are highly efficient, cleaner and more environmentally friendly for actuation compared to other power sources such as hydraulics especially with the recent improvement in power to weight ratio. This paper reviews the relative merits of electric motor and drives systems currently in use in electric vehicle systems and evaluates the possibility of employing induction machines for agricultural WSIC (tractors and implements). The electric components for the implement part include two induction electric motors, a Permanent Magnet (PM) electric motor, and an electric stepper motor with a closed loop speed, and torque control. The electric components for the tractors include a generator and its controllers, a rectifier, inverters, and an appropriate power interface. To enhance the performance of the employed induction machine, a MATLAB simulation and a typical experimental test have been carried out to compare an off-the-shelf Squirrel Cage Induction (SCI) machine with another one of the same type but employing an auxiliary winding. The new SCI machine will be called “modified” in the rest of the paper. The results show significant improvement in the performance of the modified machine with a power factor of almost 0.99, a decrease in losses of 27% and a noticeable reduction of in-rush current.

In this paper, an effective procedure for broken rotor bar (BRB) fault detection in a three-phase squirrel-cage induction machine (SCIM) is proposed. This approach relies on a motor current signature analysis (MCSA) by observing the specific fault-related current component generated by applying the DC injection braking method. Unlike the traditional MCSA, which is commonly focused on the detection of BRB sidebands around the fundamental current component, the proposed methodology introduces a new BRB feature in the current spectrum which makes it much easier for identification. The distinctive time-frequency evolution pattern of this feature provides the reliable identification of BRBs, even under no-load operating conditions, thus overcoming the major drawback of traditional MCSA-based methods. Fault severity classification is easily performed through the magnitude inspection of the BRB fault-related current component. In addition, the proposed approach does not require high-complexity signal processing algorithms to achieve reliable results. The proposed concept is presented theoretically, assisted by a magnetically coupled multiple circuit model of the SCIM, both with healthy and faulty rotor bars. Finally, the experimental tests validate the proposed methodology and demonstrate its effectiveness and usefulness.

The knowledge of Broken Rotor Bars (BRB) fault characteristic frequencies and amplitudes is an absolute must in the diagnosis of Squirrel Cage Induction Machine (SCIM). In order to deal with these problems, a numerical simulation model is usually implemented. In fact, some companies use modelling and simulation technique for designing, developing and diagnosis their new product. This paper presents an accurate approach for modeling and simulating the SCIM, under nominal and degraded conditions, including BRB fault. The techniques used here are based on Stator Current envelopes Analysis (SCEA). The performance of the model was assessed by comparing the simulation and experimental results. The results shown in this paper approve the effectiveness of the model, and allow detection of these defects.

Power converters burn-in test consumes large amount of energy, which increases the cost of testing, and certification, in medium and high power application. A simple test configuration to test a PWM rectifier induction motor drive, using a Doubly Fed Induction Machine (DFIM) to circulate power back to the grid for burn-in test is presented. The test configuration makes use of only one power electronic converter, which is the converter to be tested. The test method ensures soft synchronization of DFIM and Squirrel Cage Induction Machine (SCIM). A simple volt per hertz control of the drive is sufficient for conducting the test. To synchronize the DFIM with SCIM, the rotor terminal voltage of DFIM is measured and used as an indication of speed mismatch between DFIM and SCIM. The synchronization is done when the DFIM rotor voltage is at its minimum. Analysis of the DFIM characteristics confirms that such a test can be effectively performed with smooth start up and loading of the test setup. After synchronization is obtained, the speed command to SCIM is changed in order to load the setup in motoring or regenerative mode of operation. The experimental results are presented that validates the proposed test method.

This paper discusses the analysis of the effect of the permeance function harmonics induced by dynamic air-gap eccentricity in squirrel cage induction machines and explains the mechanism of generation of the harmonic components in the stator current spectrum. An analytical expressions of the motor inductance have been derived using the decomposition into Fourier series of both winding function approach (WFA) and the permeance function. Experimental results have been presented to validate the analytical and simulation results.

Eccentricity faults more or less exist in all rotating electrical machines. This paper establishes a more precise model of dynamic eccentricity (DE) in electrical machines named as curved dynamic eccentricity. It is a kind of axial unequal eccentricity which has not been investigated in detail so far but occurs in large electrical machines. The inductances of a large three-phase squirrel-cage induction machine (SCIM) under different levels of curved DE conditions are evaluated using winding function approach (WFA). These inductances include the stator self and mutual inductances, rotor self and mutual inductances, and mutual inductances between stator phases and rotor loops. A comparison is made between the calculation results under curved DE and the corresponding pure DE conditions. It indicates that the eccentricity condition will be more terrible than the monitored eccentricity based on the conventional pure DE model.

Incipient fault detection of the induction machines (IM) prevents the unscheduled downtime and hence reduces maintenance costs. Condition monitoring, signal processing and data analysis are the key parts of the EVI fault detection scheme. The Motor Current Signature Analysis (MCSA) is considered as an effective condition monitoring method in any EVI. However, a signal processing technique, which enhances the fault signature and suppress the dominant system dynamics and noise, must be considered. Windowed Fourier transform analysis and wavelet are of the most considered signal processing methods. However, some parameters influence their ability and accuracy. This paper intends to investigate the effectiveness of these methods for incipient fault detection. Accordingly, current signal was measured and analyzed for broken rotor bar diagnosis in a squirrel-cage induction machine. Results indicated that though windowing improves Fourier transform analysis, it is not capable of accurate incipient fault detection. In other words, wavelet analysis is superior for this purpose.

Condition monitoring, signal processing and data analysis are the key parts of the induction machine (IM) fault detection scheme. Frequency analysis of the signal acquired through the condition monitoring is commonly applied for motor fault detection. This paper is intended to investigate the ability and effectiveness of Fourier analysis of motor current for broken rotor bar detection. Accordingly, the current signals at different amount of load were measured and analyzed for broken rotor bar diagnosis in a squirrel-cage induction machine.

This paper presents the dynamic eccentricity (DE) in the squirrel cage induction machine obtained by the simulation using finite element method (FEM) and the experimental tests. Motor current signature analysis (MCSA) through the power spectral density (PSD) is used to monitor the low frequency components related to the rotor faults. In order to generalise the study, a comparison was performed between faulty machines and healthy, under different load conditions. The experimental tests show the encountered difficulties in the detection of the dynamic eccentricity under various load conditions because the spectral representation shows the existence of low frequency components for the healthy case, which are superimposed with the indicator components. These low frequency components offer an alternative way to the monitoring of eccentricity air-gap when the load is balanced.