Eccentricity Fault In Induction Motors Research Articles

A Modified Winding Function Theory and Firefly Algorithm based Optimization for Diagnosis of Eccentricity in Induction Motors

In past, several analytical models has been developed to establish the occurrence of various induction motor faults. Some of the primarily occurring faults in induction motor being; bearing and stator winding faults and to a smaller extent the other types such as rotor and eccentricity. Though there has been a lot of research work done in detection and diagnosis, still the recognition of eccentricity fault in induction motors, specifically deciding the precise degree of eccentricity is a challenging task for the researchers. In this paper, a novel model-based approach has been developed for the detection of exact type and degree of eccentricity fault present in the motor. Simulation of induction motor for both healthy and eccentricity fault has been carried out using the Modified Winding Function (MWF) approach. A procedure has been developed for estimating the eccentricity fault from simulation model. Also, a metaheuristic nature inspired algorithm, viz. Firefly Algorithm (FA) has been used for the optimization of results obtained from simulation model. The results obtained from experimental investigations are close to that obtained from simulations, confirming the applicability of used model and simulation approach to find the exact degree of eccentricity defect.

Model‐based exact technique to identify type and degree of eccentricity faults in induction motors

In spite of numerous research activities performed so far, determination of type and exact degree of the eccentricity faults in induction motors is challenging yet. This study presents a model-based fault diagnosis technique for identifying all the three eccentricity fault types, including static, dynamic and mixed eccentricities, with their exact degrees in the mentioned motors. This technique uses a special analytic model prepared for simulating squirrel-cage induction motors under healthy and all eccentricity fault conditions as well as the particle swarm optimisation method. Correct performance and effectiveness of the proposed technique is verified by using simulation and experiments. Finally, an algorithm is introduced for the eccentricity fault treatment using the identified degrees of the fault components and their time trends.

Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools

The present work is focused on the diagnosis of mixed eccentricity faults in induction motors via the study of currents demanded by the machine. Unlike traditional methods, based on the analysis of stationary currents (Motor Current Signature Analysis (MCSA)), this work provides new findings regarding the diagnosis approach proposed by the authors in recent years, which is mainly focused on the fault diagnosis based on the analysis of transient quantities, such as startup or plug stopping currents (Transient Motor Current Signature Analysis (TMCSA)), using suitable time-frequency decomposition (TFD) tools. The main novelty of this work is to prove the usefulness of tracking the transient evolution of high-order eccentricity-related harmonics in order to diagnose the condition of the machine, complementing the information obtained with the low-order components, whose transient evolution was well characterised in previous works. Tracking of high-order eccentricity-related harmonics during the transient, through their associated patterns in the time-frequency plane, may significantly increase the reliability of the diagnosis, since the set of fault-related patterns arising after application of the corresponding TFD tool is very unlikely to be caused by other faults or phenomena. Although there are different TFD tools which could be suitable for the transient extraction of these harmonics, this paper makes use of a Wigner–Ville distribution (WVD)-based algorithm in order to carry out the time-frequency decomposition of the startup current signal, since this is a tool showing an excellent trade-off between frequency resolution at both high and low frequencies. Several simulation results obtained with a finite element-based model and experimental results show the validity of this fault diagnosis approach under several faulty and operating conditions. Also, additional signals corresponding to the coexistence of the eccentricity and other non-fault related phenomena making difficult the diagnosis (fluctuating load torque) are included in the paper. Finally, a comparison with an alternative TFD tool – the discrete wavelet transform (DWT) – applied in previous papers, is also carried out in the contribution. The results are promising regarding the usefulness of the methodology for the reliable diagnosis of eccentricities and for their discrimination against other phenomena.

Distinguishing Load Torque Oscillations and Eccentricity Faults in Induction Motors Using Stator Current Wigner Distributions

This paper proposes a novel diagnosis method for detection and discrimination of two typical mechanical failures in induction motors by stator current analysis: load torque oscillations and dynamic rotor eccentricity. A theoretical analysis shows that each fault modulates the stator current in a different way: torque oscillations lead to stator current phase modulation, whereas rotor eccentricities produce stator current amplitude modulation. The use of traditional current spectrum analysis involves identical frequency signatures with the two fault types. A time-frequency analysis of the stator current with the Wigner distribution leads to different fault signatures that can be used for a more accurate diagnosis. The theoretical considerations and the proposed diagnosis techniques are validated on experimental signals.

Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization

Much research works address model-free or signal processing and spectral analysis-based fault detection schemes for rotor eccentricity fault in induction motors. Nevertheless, despite existing reliable fault-embedded eccentricity mathematical models such as the winding function method an integrated model-based fault detection algorithm for detecting this fault yet has not been fully explored. This article presents model-based mixed-eccentricity fault detection and diagnosis for induction motors. The proposed algorithm can successfully detect faults and their severity using stator currents. To determine the values of the fault-related parameters, an adaptive synchronization-based parameter estimation algorithm is introduced using particle swarm optimization. Simulation and experiments demonstrate the ability of the algorithm to detect and diagnose these faults. The proposed algorithm can be employed to estimate the parameters, in addition to slowly time varying and abruptly changing parameters.

Using a Surge Tester to Detect Rotor Eccentricity Faults in Induction Motors

This paper discusses a new method to detect rotor eccentricity faults in induction motors using a so-called surge test that is applied to the stator windings. Rotor eccentricity faults cause an asymmetrical air gap in induction motors, which makes the surge waveform shape depend on rotor position. The change of surge waveform per revolution can be used to indicate any abnormal air gap problem clearly. The feasibility of this detection scheme is verified by experiments. The surge test is a nonintrusive, cost-efficient, and reliable offline method that is particularly useful for routine and predictive maintenance.