Eccentricity Diagnosis Research Articles

Development of a diagnostic system for the presence of rotor eccentricity in traction induction motors

The aim of research is to develop a system for functional diagnostics of the presence of rotor eccentricity in traction induction motors of railway rolling stock. To achieve the aim, the following objectives were solved in the work: the Prony’s method was adapted to perform the task of diagnosing the eccentricity of the rotor of an induction motor during the operation of the rolling stock; the algorithm of functioning of the system of functional diagnosis of rotor eccentricity based on the adapted Prony’s method is proposed; a structural diagram of the rotor eccentricity diagnosis unit based on the adapted Prony’s method is proposed; a structural diagram of the system of functional diagnosis of rotor eccentricity is proposed. The most important results are obtaining a mathematical model of the Prony’s method, adapted to the task of diagnosing the eccentricity of the rotor of an induction motor during the operation of rolling stock. The adaptation of the Prony’s method algorithm is performed by applying the Wiener-Hopf procedure to the analyzed signals. This will make it possible to apply the proposed algorithm in conditions where the process of changing the analyzed signals is stochastic in nature. This will make it possible to determine the eccentricity degree of the AM rotor with greater accuracy and to make a more correct decision regarding the AM operation with the existing eccentricity of the rotor. Key words: induction motor, diagnosis, eccentricity, rolling stock.

Diagnosis of Static Eccentricity in 400 kW Induction Machine Based on the Analysis of Stator Currents

Current spectrums of a four pole-pair, 400 kW induction machine were calculated for the cases of full symmetry and static eccentricity. The calculations involve integration of 93 electrical plus four mechanical ordinary differential equations. Electrical equations account for variable inductances affected by slotting and eccentricities. The calculations were followed by Fourier analysis of the stator currents in steady state operation. Zooms of the current spectrums, around the 50 Hz fundamental harmonic as well as of the main slot harmonic zone, were included. The spectrums included in this paper are referred to both calculated and measured currents.

Diagnosis of Mixed Eccentricity in 400 kW Induction Machine Based on Inspection of Stator Current Spectrums

This paper dealing with diagnosis of induction machines affected by combined eccentricity via analyzing the sator curents, inspection of the current spectrums and predict the harmonics that present in the spectrums and represent an indication for this ailment . The machine under invistigation is a 400 kW, four pole-pair, squarelcage induction machine. The current spectrums were calculated for the cases of full symmetry and mixed eccentricity. The calculations presented in this paper, are based on the poly-harmonic model that accounting for static and dynamic eccentricity, stator and rotor slotting, parallel branches as well as cage asymmetry. The calculations were followed by Fourier analysis of the stator currents in steady state operation. The inspection of the current spectrums focusing on two main zones these are the 50 Hz fundamental harmonic zone and the main slot harmonic zone. The paper will include the zooms of the above mentioned zones to demonstrate the harmonics presented in each zone. The calculated spectrums will be compared with the spectrums of the real measured currents.

Online Airgap Flux Based Diagnosis of Rotor Eccentricity and Field Winding Turn Insulation Faults in Synchronous Generators

Rotor faults in a synchronous generator severely deteriorate its performance, reducing the operation efficiency and reliability. Most of the field-wide accepted methods require stoppage of generator operation and disassembly, and online diagnosis of rotor faults is difficult due to the lack of dependable online methods for sensitive rotor fault detection. Therefore, online methods that can reliably and unambiguously detect rotor field winding and eccentricity faults before they lead to machine failure are required. This paper presents a new method for detecting and distinguishing rotor field winding insulation and eccentricity faults online during synchronous generator operation based on measurements from airgap flux probes, which are installed in most synchronous generators used for electric power generation. A non-complex, time domain analysis method is proposed which makes use of the voltage induced in the airgap flux sensor for a reliable prediction of the presence and identification of rotor field winding turn insulation failure and rotor dynamic eccentricity. The method is tested and verified experimentally on a 5 hp synchronous generator and on measurements from a 330 MW pumped storage generator.

Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation.

Since it is not efficient to physically study many machine failures, models of faulty induction machines (IMs) have attracted a rising interest. These models must be accurate enough to include fault effects and must be computed with relatively low resources to reproduce different fault scenarios. Moreover, they should run in real time to develop online condition-monitoring (CM) systems. Hybrid finite element method (FEM)-analytical models have been recently proposed for fault diagnosis purposes since they keep good accuracy, which is widely accepted, and they can run in real-time simulators. However, these models still require the full simulation of the FEM model to compute the parameters of the analytical model for each faulty scenario with its corresponding computing needs. To address these drawbacks (large computing power and memory resources requirements) this paper proposes sparse identification techniques in combination with the trigonometric interpolation polynomial for the computation of IM model parameters. The proposed model keeps accuracy similar to a FEM model at a much lower computational effort, which could contribute to the development and to the testing of condition-monitoring systems. This approach has been applied to develop an IM model under static eccentricity conditions, but this may extend to other fault types.

A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research.

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.

Study of a Combined Demagnetization and Eccentricity Fault in an AFPM Synchronous Generator

This article investigates the combined partial demagnetization and static eccentricity fault in an Axial Flux Permanent Magnet (AFPM) Synchronous Generator. The machine is simulated using 3D FEM, while the EMF spectrum is analyzed in order to export the fault related harmonics using the FFT analysis. Firstly, the partial demagnetization fault, without the coexistence of eccentricity, and both the static angular and axis eccentricity faults, without the coexistence of partial demagnetization, are studied. In the case of eccentricity fault, the phase EMF sum spectrum has also been used as a diagnostic mean, because, when only eccentricity fault exists in the generator (either angular or axis) new harmonics do not appear in the EMF spectrum. Secondly the combination of partial demagnetization fault with static axis and static angular eccentricity is investigated and different comparisons are made when the demagnetization and the eccentricity level changes. The investigation revealed that the combination of eccentricity and demagnetization creates new harmonics in the EMF spectrum. The novelty of the article is that these combined faults are studied for the first time in the international literature, and the phase EMF sum spectrum has not been previously used for eccentricity diagnosis in this machine type.

Induction machine model with space harmonics for the diagnosis of rotor eccentricity, based on the convolution theorem

Condition based maintenance (CBM) systems of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults. Such models are needed to develop new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, these models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. However, the development of a fast, analytical model of the eccentric IM is a challenging task, due to the non-uniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, first enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position, taking into account the non-uniform air-gap length and the actual position of all the stator and rotor conductors. Once these inductances are calculated, they are used in a coupled circuits analytical model of the IM, which in this way is able to calculate the time evolution of the electrical and mechanical quantities that characterize the machine functioning, under any type of eccentricity. Specifically, the model is able to reproduce accurately the characteristic eccentricity fault related harmonics in the spectrum of the stator current.The proposed approach is validated through two different methods. First, using a finite elements (FEM) model, in order to validate the correctness of the proposed method for calculating self and mutual inductances, taking into account the non-uniform air-gap. Finally, through an experimental test-bed using a commercial induction motor with a forced mixed eccentricity fault, in order to validate that the full model correctly reproduces the phase currents in such a way that their spectra accurately show the harmonics related with the eccentricity fault, which are the basis of many MCSA diagnostic approaches.

압연기의 편심 원인 자동진단을 위한 신경망 설계

This paper presents a design of neural network for automatically diagnosing eccentric cause in rolling machine. An applicable structure of neural network for diagnosing eccentric cause is designed. A generating method of input and output data relating each type of eccentricity is proposed for learning neural network. The suggested neural network worked within the specified error range through learning input and output data for learning. According to computer simulation, the suggested neural network outputs stably desired eccentricity diagnosis results for input data.

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.

Diagnosis of Static Eccentricity in 3-Phase Synchronous Machines using a Pseudo Zero-Sequence Current

Large synchronous generators are the heart of the modern world, while producing the vast majority of the electric power consumed globally. Although they are robust devices, they are prone to degradation and failure. If such failures are not detected at an early stage, then the negative impact may be catastrophic in terms of financial costs, repair times, human lives and quality of life. This is the reason for continuous research in the field of condition monitoring aiming towards the reliable operation of synchronous generators. This paper proposes a novel technique for the diagnosis of the static eccentricity in synchronous generators. The proposed approach is off-line and non-intrusive, allowing the estimation of the fault severity with stator current measurements only. The performed work has been carried out with Finite Element Simulations and extensive experimental testing.

Analysis of Permanent Magnet Synchronous Machine Tooth Flux Under Eccentricity Fault

Eccentricity fault is a common fault of permanent magnet synchronous machines. It is hard to repair the fault efficiently if it is not diagnosed accurately. This paper presents a quantitative analysis of tooth flux with eccentricity fault based on co-simulation. Moreover, set up an experiment platform to validate the analysis. The analysis result reflects quantitative relationship of the eccentricity ratio and the tooth fluxes. It is helpful for improving the accuracy of the eccentricity diagnosis based on the tooth flux and the reliability of PMSMs.

Eccentricity Failure Detection of Brushless DC Motors From Sound Signals Based on Density of Maxima

Brushless Direct Current (BLDC) motors have been used in a wide range of fields. In some critical applications, failures in these machines can cause operational disasters and cost lives if they are not detected in advance. The classical methods for detecting incipient faults in BLDC motors perform processing of the current signal to obtain the required information. In this work, the SAC-DM (Signal Analysis based on Chaos using Density of Maxima) technique is applied for the first time in the diagnosis of failures of electromechanical systems from sound signals. Wavelet Multiresolution Analysis (WMA) is used to separate a chaotic signal component from the sound emitted by the motor. This work demonstrates that it is feasible to perform dynamic eccentricity diagnosis in BLDC motors by identifying variations of the SAC-DM of the sound signal. The technique exposed in this work requires low computational cost and achieves high success rate. To validate the method, tests were carried out on a small BLDC motor normally used in Unmanned Aerial Vehicle (UAV), demonstrating the ability of the method to detect the speed of the motor in 95.89% of the cases and to detect eccentricity problems at a fixed speed in 88.34% of the cases.

Advanced Diagnosis of Rotor Faults and Eccentricity in Induction Motors Based on Internal Flux Measurement

Most of the existing electrical techniques of condition monitoring and fault diagnosis are based on frequency analysis of signals measured externally to the motor. In this paper, it is proposed to add the space dimension to the measured signals by using an array of Hall effect flux sensors installed inside the motor air gap, and to explore the advantages brought by such an instrumentation. The paper presents analytical models of air gap flux density expressed in terms of time and space, for a healthy motor and for a motor with rotor bar faults and mixed eccentricity. Based on these models, the paper proposes a new technique for early detection of rotor bar faults and mixed eccentricity, and for discrimination between them. The paper demonstrates that, with the help of the proposed technique, the severity of each fault and its location relative to rotor or stator can be accurately established. The results of the presented study are supported by simulations and validated by experiments on a laboratory scale induction motor. The formulated fault diagnosis algorithm is suitable for real-time implementation.

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air-gap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

Fault Eccentricity Diagnosis in Variable Speed Induction Motor Drive Using DWT

Fault Eccentricity Diagnosis in Variable Speed Induction Motor Drive Using DWT

The Use of the Modified Prony’s Method for Rotor Speed Estimation in Squirrel-Cage Induction Motors

It is well known that rotor speed estimation assumes a paramount importance for the correct diagnosis of bearings, air-gap eccentricities, or rotor bar defects. In this paper, a new technique for rotor speed estimation using a modified Prony's method is proposed. The algorithm developed for this purpose is based on tracking the frequencies of the rotor slot harmonics (RSH) that exist in stator currents of most squirrel-cage induction motors. High-order RSH frequencies are used to avoid the possible effect of harmonics stemming from other sources. The proposed modified Prony's method shows a great ability for tracking RSH frequencies and then the rotation speed. In addition, this technique can deal with noisy and nonstationary signals and it requires only few data samples, which reduce considerably the computational time and data storage requirements. Consequently, the proposed algorithm is suitable for online implementation. The method's effectiveness is verified by simulation and experimental tests.

Eccentricity fault diagnosis in three‐phase‐wound‐rotor induction machine using numerical discrete modeling method

SummaryIn recent years, several papers have dealt with eccentricity fault diagnosis considering the cage‐induction machine while wound machine type has not been studied as a case. In this paper, eccentricity fault is studied based on the current/torque signature considering modified winding function approach (MWFA). A novel discrete model is introduced, and eccentricity fault is evaluated in both static and dynamic cases. To reach this aim, a numerical model is presented for slot openings, distributed windings and air‐gap, thereby computing the machine's inductances using MWFA. Power spectral density (PSD) of stator's current and machine's vibration are analyzed for static and dynamic eccentricity diagnosis and the finite element method (FEM) is utilized to precisely verify the proposed method. Mathematical‐based model with consideration of both slot openings and non‐sinusoidal winding function effects are the advantages of the proposed method over the previous researches in this field. Moreover, proposed method, which is based on mathematical modeling, could be easily applied to other types of electrical machines, and this feature is regarded as another key point of this paper. Copyright © 2016 John Wiley & Sons, Ltd.

Modern Diagnostics Techniques for Electrical Machines, Power Electronics, and Drives

For the last ten years, at least three different special sections dealing with diagnostics in power electrical engineering have been published in the IEEE transactions on industrial electronics [1]-[5]. All of them had their specificities, but the last ones, starting in 2011, were more connected to relevant events organized on the topic. In fact, these events have been clearly the only international forums fully dedicated to diagnostics techniques in power electrical engineering. For this particular issue, it has been decided to separate the different submissions into six parts: state of the art; general methods; induction machines (IMs); synchronous machines (SMs); . electrical drives; power components and power converters. The second section includes only one state-of-the-art paper, which is dedicated to actual techniques implemented in both industry and research laboratories. The third section includes three papers on diagnostic techniques not specifically aimed at a particular type of machine. The fourth section includes three papers devoted to diagnostics of rotor faults, two dedicated to stator insulation issues, and four papers dealing with mechanical faults diagnosis in IMs. The fifth section includes papers focusing on different types of SMs. The first two papers deal with wound-rotor SMs, the following three papers are dedicated to permanent-magnet radial flux machines, and the last one deals with permanent-magnet axial flux machines. Regarding the types of faults analyzed, there are three papers devoted to the diagnosis of interturn short circuits in the stator windings, i.e., one dedicated to the detection and location of field-winding-to-ground faults and a paper devoted to the diagnosis of static eccentricities. In the sixth section, two papers investigate issues related to faults in drive sensors, and one is devoted to fault detections in the coupling inductors. The last section includes two papers devoted to diagnosis of faults and losses analysis in switching components of power converters.