Eccentricity Fault Detection Research Articles

Detection and classification of air gap eccentricity fault in Induction machine using artificial intelligence techniques

Detection and classification of air gap eccentricity fault in Induction machine using artificial intelligence techniques

DPMSLM Eccentricity Fault Detection Based on Multiview of Mystery Curve Transformation and Deep Feature Extraction

DPMSLM Eccentricity Fault Detection Based on Multiview of Mystery Curve Transformation and Deep Feature Extraction

Mixed Eccentricity Fault Detection for Induction Motors Based on Time Synchronous Averaging of Vibration Signals

This paper presents a method based on vibration signals to diagnose rotor mixed eccentricity faults in three-phase squirrel cage induction motors (SCIMs). Rotor eccentricity is a typical fault in SCIMs that significantly affects the performance of electrical machines and results in unwanted vibrations. The time synchronous averaging (TSA) signal, which consists of the harmonics components generated by the fault, is obtained via the characteristic frequency of the eccentricity fault. TSA is decomposed into two signals: TSA-regular and TSA-difference, which are respectively similar to approximate and details of wavelet transform. Based on the fault index, which is the maximum FFT value of the TSA-difference signal, the healthy and faulty motors can be distinguished. The proposed approach is evaluated on an experimental test rig equipped with a 1.5 kW SCIM supplied directly from the power grid under various load conditions. The results illustrate the effectiveness of the proposed approach in detecting the rotor mixed eccentricity faults.

Induction Motor Eccentricity Fault Detection and Quantification Using Topological Data Analysis

Induction Motor Eccentricity Fault Detection and Quantification Using Topological Data Analysis

Empirical mode decomposition and Hilbert–Huang transform-based eccentricity fault detection and classification with demagnetization in 120 kW interior permanent magnet synchronous motors

Permanent magnet synchronous motors (PMSMs) are extensively used in electric vehicles (EVs) owing to their high power density and efficiency. However, their safety and reliability have not been verified sufficiently. Failure of PMSMs in EVs can cause performance degradation, increased maintenance costs, and catastrophic accidents. Therefore, early detection of faults in PMSMs is necessary for an efficient and safe operation. In this study, static, dynamic, and mixed eccentricity faults in 120 kW interior permanent magnet synchronous motors (IPMSMs) were diagnosed and distinguished from uniform demagnetization fault by monitoring the Hilbert spectrum of the stator current. Experiments were carried out for different speeds, loads, and fault severities to analyze the effect of operating conditions on the proposed fault detection method. The phase current data were decomposed into set of intrinsic mode functions (IMFs) with empirical mode decomposition (EMD) method and Hilbert transform was applied for each IMFs to calculate the instantaneous frequency and energy. The resultant Hilbert spectrum exhibited instantaneous frequency distortions with an instantaneous energy surge under an eccentricity fault. The effect of thresholding in EMD was analyzed to improve the accuracy of the proposed method under high-load and high-speed conditions. Also, the experimental results showed that eccentricity and uniform demagnetization faults can be classified by monitoring the overall instantaneous energy.

Eccentricity fault detection in surface-mounted permanent magnet synchronous motors by analytical prediction, FEM evaluation, and experimental magnetic sensing of the stray flux density

The paper discusses the impact of rotor eccentricity in permanent magnet synchronous motors (PMSMs), which leads to increased motor vibration and unbalanced magnetic pull, resulting in accelerated aging of motor parts. Undetected eccentricity in the motor can exacerbate these issues and lead to a higher risk of stator-rotor contact and forced outages. The study proposes a technique for detecting eccentricity faults using an analog magnetic field sensor outside the motor, without requiring the sensor to be installed within the motor. The proposed technique was evaluated using analytical calculations, 2-D finite elements, and experimental results. The proposed method was evaluated in different scenarios and it was found to be capable of providing detection and identification of parallel and inclined eccentricity. The size of the magnetic field at a specific point in the outer space of the motor was estimated to be 26μT, 30μT, and 43μT, respectively, through analytical modeling in healthy conditions, 10% and 30% eccentricity, and it was confirmed by experimental testing. Since the proposed method does not require installing sensors or search coils within the motor, such a method is more appropriate for practical applications and improving the cost-performance in online condition monitoring. In addition, with some specific modifications, the suggested concept becomes useful for fault detection in other rotating machinery types.

Eccentricity fault detection in synchronous reluctance machines

Eccentricity fault detection in synchronous reluctance machines

Detection of Rotor Field Winding, Damper Bar, and Eccentricity Faults in Salient Pole Synchronous Machines With the Pole Drop Test

The pole drop test is the most widely applied means of detecting shorted turns in the rotor field winding of salient pole wound field synchronous machines (WFSM). It is a simple off-line test that relies on detecting the asymmetry between the voltage drop in the windings of each pole with ac voltage applied to the field winding terminals. Although it can provide sensitive detection of shorted field turns, it is also influenced by asymmetries in the motor produced by defects or imperfections in the rotor poles or damper bars. In this work, the feasibility of using the pole drop test for detecting damper bar and eccentricity faults in addition to shorted field windings is investigated for the first time. The change in the impedance of each pole under the fault conditions is analyzed from the electrical equivalent circuit and finite element (FE) simulation. Based on the analysis, it is shown that the 3 faults can be detected and classified from the impedance or voltage distribution with the pole drop test. Experimental tests performed on 4 pole, 30 kVA and 6 pole, 5.5 kVA salient pole WFSMs confirm that the 3 faults can be identified as claimed.

Static and Dynamic Eccentricity Faults Diagnosis in PM Synchronous Motor Using Planar Search Coil

Eccentricity fault in electrical motor causes increased noise and vibration, and degradation of motor characteristics. Thus, early detection of the eccentricity fault is required. Motor current signal analysis, a common fault detection method, may fail to detect eccentricity faults, depending on the type of eccentricity. This paper proposes an eccentricity fault detection method using a new planar search coil (PSC). The model of PSC and the contribution of eccentricity to PSC are presented. PSC is a new type of search coil that generates a voltage signal from magnetic flux. The output signal of PSC is changed by the inductance deviation, a result of the rotor eccentricity. Finally, the experimental verification of fault detection using PSC is shown. The eccentricity fault detection method using PSC is proven to diagnose both dynamic eccentricity and static eccentricity faults.

An effective Hilbert-Huang transform-based approach for dynamic eccentricity fault diagnosis in double-rotor double-sided stator structure axial flux permanent magnet generator under various load and speed conditions

Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert-Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault diagnosis since it is unaffected by transient conditions which make the diagnosis process incur into false alarms. The HHT-based methods has been demonstrated in recent years for rotor and bearing faults detection of induction machine and also for stator faults identification in PM synchronous machines with radial flux structure. This study explores the possibility of applying the technique to the detection of dynamic eccentricity faults in double-rotor double-sided stator structure axial flux permanent magnet generator under variable load and speed conditions. This approach relies on two steps: estimating the intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) and computing the instantaneous amplitude (IA) and instantaneous frequency (IF) of IMFs using the Hilbert transform. The more significant IMFs are determined by means of Hilbert spectrum, which is applied for accurate eccentricity fault diagnosis. The eccentricity severity can be evaluated based on the IMFs energy value. The theoretical basis of the proposed method is presented. The effectiveness of the proposed method is verified by a series of simulation and experimental tests under different conditions. The results show that the presented approach in this paper is robust against load and speed variations.

Online Detection and Location of Eccentricity Fault in PMSG With External Magnetic Sensing

The detection and location of eccentricity faults in a permanent magnet synchronous generator (PMSG) become increasingly essential, especially in safety-critical applications. However, the existing approaches for monitoring eccentricity subject to many limitations. In this article, a novel method for online detection and location of the eccentricity fault in a PMSG is proposed based on the external magnetic sensing. The noninvasiveness facilitates installation and maintenance work. In this article, the effect of different eccentricity faults on the external leakage flux density is analyzed theoretically with the magnetic equivalent circuit method first. Then, two indicators are proposed for detecting static eccentricity (SE) and dynamic eccentricity (DE), respectively. The amplitudes of the SE and DE indicators are proportional to the fault degree. The angle of the SE indicator proves to be the direction of SE. Meanwhile, two indicators show excellent robustness and are independent of loads and rotation speed. Moreover, these indicators can be applied in any surface-mounted PMSG with a three-multiple number of slots. Accordingly, an algorithm to diagnose the eccentricity fault is shown based on two indicators from three sensing points outside. The computation of the method is light, which leads to fast detection speed. Finally, the accurate results in multioperating conditions from simulations and experiments verify the feasibility of the proposed approach.

Air-gap eccentricity fault detection, isolation, and estimation for synchronous generators based on eigenvalues analysis

Air-gap eccentricity fault causes rotor–stator rub and consequently damage to Synchronous Generators (SGs). In this paper, a fault diagnosis approach to diagnose the eccentricity fault for SGs is presented. In this approach, the state matrix eigenvalues based on the subspace identification are estimated, and those are used for fault diagnosis. Two dq models of SGs in faulty and healthy conditions are employed to present the theoretical foundation of the method. As the main advantage, the introduced fault diagnosis method is working properly for either linear or nonlinear loads of SGs. The stator and field currents and voltages, and rotor rotational speed are required signals in the introduced approach. The method is validated using experimental data of SGs in Iran-Safir (ER24) diesel–electric locomotives.

Broken Rotor Bar and Rotor Eccentricity Fault Detection in Induction Motors Using a Combination of Discrete Wavelet Transform and TeagerKaiser Energy Operator

In this paper, a hybrid approach is proposed to detect the broken rotor bar and rotor mixed eccentricity faults of three-phase squirrel cage induction motors based on one phase of the stator current signal. The hybrid method is based on a combination of discrete wavelet transform (DWT), Teager–Kaiser energy operator (TKEO) and power spectral density (PSD). At the first stage, DWT is applied on the stator current signal; then TKEO is applied on some of the DWT detail coefficients and finally PSD is used to extract the required feature to detect the aforementioned faults. The proposed method is tested on an experimental test-rig having a 1.5 kW induction motor under various load conditions and three different power supplies: 1) directly supplied by power grid; 2) supplied by a drive system based on scalar control technique; and 3) supplied by a drive system based on direct torque control (DTC) technique. The results show the efficacy of the proposed method in accurate detection of the broken rotor bar and rotor mixed eccentricity faults.

A novel detection method for diagnosis of rotor eccentricity in three-phase induction motor

In this paper, a novel method to diagnose eccentricity fault in rotor of three-phase induction motor based on the current signal, is proposed along with experimental validation. It utilizes three-phase current in time series into Clarks two phase α–β 90° apart vectors assimilating into 0° measuring reference frame starting point with the aid of key-phasor averaging method. α–β sinusoidal waves are applied to an interpolation method which gives an improved Fast Fourier transform of amplitude and phase angle of α–β components. These transformed two-phase current signals are again measured to get amplitude and phase. Finally, the amplitude and phase of α–β are used to generate holo-spectrum with an ellipse of the first order. Since unbalance fault occur at peak fundamental 1X harmonic frequency component, so its amplitude and phase angle are utilized for orbit construction. A comparison is made on rotor orbit and its eccentricity values, constructed from vibration signals and three-phase current signals, both measured simultaneously. This leads to a direct relationship in terms of orbit eccentricity and its longitudinal major axis inclination. The results show that at low frequency the eccentricity values of vibration and current signals orbits coincide hence the unbalance fault is detectable in induction motor by only utilizing the current signals. This method represents rotor non-linear dynamic behavior and is immune to unsymmetrical and anisotropic surface properties of the rotor and can measure rotor condition representing an accurate severity level of air gap eccentricity or, in mechanical terms, rotor unbalance fault. The new technique is a progressive development in induction motor air gap eccentricity fault detection and is applicable to harsh running conditions due to its simplified and non-invasive approach.

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Flux-Based Detection and Classification of Induction Motor Eccentricity, Rotor Cage, and Load Defects

Motor current signature analysis (MCSA) has been accepted in the field as a reliable means of detecting faults in the rotor cage of induction motors. However, there are many limitations to MCSA-based detection for other types of faults including rotor eccentricity and load defects. Recently, there has been increasing interest in airgap or leakage flux monitoring as an alternative to replace or complement MCSA. Flux monitoring can provide a reliable means of detecting rotor faults since anomalies in the rotor magnetomotive force or airgap can be directly observed. In this article, a new method based on monitoring the attenuation of rotor rotational frequency sidebands in the flux spectra is proposed for reliable detection and classification of rotor cage and eccentricity faults. It is also shown that flux monitoring can provide detection of rotor faults that is insensitive to the load defects. An experimental study under controlled broken bar, eccentricity, misalignment, and load unbalance conditions are given to support the claims. The results show that rotor cage, eccentricity, and load defects can be detected and distinguished for cases where MCSA alone is ineffective. A comparative evaluation between the proposed flux monitoring method and existing methods (MCSA, vibration analysis) is also given.

Eccentricity fault detection in brushless doubly fed induction machines

A new fault diagnosis method for detecting the rotor eccentricity faults including static, dynamic and mixed eccentricity is proposed for brushless doubly-fed induction machines (BDFIMs). BDFIMs are attractive alternatives for the conventional doubly-fed induction generator (DFIG) for offshore wind power generation; therefore, paying attention to their fault detection is essential. Existing fault detection methods for conventional induction machines cannot be directly applied to the BDFIM due to its special rotor structure and stator winding configurations as well as complex magnetic field patterns. This article proposes a novel fault detection method based on motor current signal analysis to determine stator current harmonics, induced by the nested-loop rotor slot harmonics (NRSHs), as fault indices. The analysis is performed under healthy conditions and with different types of rotor eccentricity. Finally, a sensitivity analysis is carried out to confirm the practicability of the proposed technique with various fault intensities and load conditions. Analytical winding function approach, finite element analysis and experimental tests on a prototype D180 BDFIM are used in this study to validate the proposed fault detection technique.

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.

Generation of Mechanical Frequency Related Harmonics in the Stray Flux Spectra of Induction Motors Suffering From Rotor Electrical Faults

Lately, the monitoring and analysis of the induction motor stray flux has been a modern trend and significant research work was accomplished. Most papers have focused on the monitoring of rotor electrical faults around the fundamental stray flux signature, imitating in this way the traditional motor current signature analysis (MCSA). However, such signatures may still have some disadvantages leading to false alarms. This is the motivation behind the use of alternative signatures, such as in this particular case and the mechanical frequency. The existence of the mechanical frequency in the stator current is still the best signature for detection of the mixed rotor eccentricity fault and load imbalances. Even healthy motors present this harmonic due to some low-level inherent eccentricity and inherent load oscillations. Despite that, it will be shown for the first time in this article that the mechanical frequency associated harmonics in the stray flux is possible to originate purely from rotor electrical faults. Those signatures present significant sensitivity to the broken bar fault severity. Moreover, they are advantageous for low load operation compared to traditional signatures. Finally, the sidebands of the mechanical frequency related harmonics are very sensitive to the broken rotor bar fault while quite immune to the number of the rotor bars.

Static Eccentricity Fault Analysis in Inverter Fed Induction Motor using Finite Element Method

One of the common faults encountered in three phase induction motors (IM) is the eccentricity fault. In this work, a study on the electromagnetic characteristics of open-loop voltage source inverter fed IM is carried out for healthy and static eccentricity fault condition with the assistance of Finite Element Analysis. The importance of electromagnetic field analysis is it contains the data about the position of stator, rotor and mechanical parameters of IM. Thus the strategy of monitoring airgap magnetic fields and current are often used for the diagnosis of faults in IM. Detection of eccentricity fault at the incipient stage is difficult because the changes that the fault would introduce in the motor terminal quantities are negligible unless the severity is very high. A comparative study is carried out for healthy and eccentric fault motor with the help of ANSYS Maxwell Finite Element Analysis tool. Electromagnetic field parameters such as speed, current, flux distribution over the machine and in the air gap are analysed for healthy and faulty motor.