Mixed Eccentricity Fault Research Articles

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.

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.

Analytical modelling of salient pole synchronous generators under eccentricity faults by including stator slotting effect

AbstractHere, a dynamic analytical model is provided for salient pole synchronous generators (SPSGs) under different eccentricity faults. A useful technique is developed first to describe the rotor position‐dependent non‐uniform air gap distribution by a proper function in the healthy state, which includes the stator slotting effect. Then, the exact air gap function is used along with the modified winding function theory to extract non‐integral equations for computing the SPSG various inductances that are playing critical role during analytical modelling and simulation of the machine. Afterword, the air gap function and the inductance equations are modified to include all types of the eccentricity fault. A 3 kW SPSG is simulated and then tested in laboratory under the healthy and the mixed eccentricity (ME) conditions. There are good accordance between the simulation and corresponding experimental results; they show that some harmonic components are introduced in the rotor and the stator currents due to the ME fault whose frequencies are integer multiples of the rotor speed frequency and whose amplitudes increase by increasing the fault severity. The load level change also affects the amplitudes of the harmonics.

Analytical Model of Eccentric Induction Machines Using the Conformal Winding Tensor Approach.

Induction machines (IMs) are a critical component of many industrial processes, and their failure can cause large economic losses. Condition-based maintenance systems (CBMs) that are capable of detecting their failures at an incipient stage can reduce these risks by continuously monitoring the IMs’ condition. The development and reliable operations of CBMs systems require rapid modeling of the faulty IM. Due to the fault-induced IM asymmetries, these models are much more complex than those used for a healthy IM. In particular, a mixed eccentricity fault (static and dynamic), which can degenerate into rubbing and destruction of the rotor, produces a non-uniform IM air gap that is different for each rotor position, which makes its very difficult to calculate the IM’s inductance matrix. In this work, a new analytical model of an eccentric IM is presented. It is based on the winding tensor approach, which allows a clear separation between the air gap and winding-related faults. Contrary to previous approaches, where complex expressions have been developed for obtaining mutual inductances between conductors and windings of an eccentric IM, a conformal transformation is proposed in this work, which allows using the simple inductance expressions of a healthy IM. This novel conformal winding tensor approach (CWFA) is theoretically explained and validated with the diagnosis of two commercial IMs with a mixed eccentricity fault.

Locally optimized chirplet spectrogram for condition monitoring of induction machines in transient regime

The locally optimized chirplet spectrogram (LOCS) is a novel method proposed in this work for generating a high-resolution and cost-effective spectrogram of the induction machine (IM) current, suitable for the identification of fault-related harmonics in transient conditions. Its distinctive novelty is that it optimizes automatically the parameters of the analysing window used for building the current spectrogram, at each point of the time–frequency plane, with the computational cost of a conventional, non-optimized spectrogram. It is based on the definition of a large dictionary of different chirplet windows, which are combined into a single, complex time window. A single short time Fourier transform with this new window generates in parallel the spectrograms of all the dictionary windows, and the LOCS chooses among them the locally optimized values in an automatic way. The proposed technique is applied to the diagnosis of two commercial induction motors with bar breakages and mixed eccentricity faults.

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.

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

Characterization of Fault Signature Due to Combined Air-Gap Eccentricity and Rotor Faults in Induction Motors

An accurate means of non-invasive condition monitoring of the popular industrial drive, three-phase squirrel-cage induction motor, can help to avoid unscheduled maintenance downtime and loss. Faults like air-gap eccentricity can exist even in a newly assembled drive and hence may co-exist with other internal defects. Despite it being a possible situation, the occurrence of simultaneous faults has seldom been studied. Therefore, there is a need for identifying fault signatures of combined fault conditions in a non-invasive manner. This paper presents a detailed model-based study on a three-phase squirrel-cage induction motor with the simultaneous existence of broken rotor-bar and air-gap mixed eccentricity faults using spectral analysis of stator current, instantaneous power, and estimated air-gap torque signals. The modelling of the machine is done using the Multiple Coupled Circuit method and modified to model the presence of the combined fault conditions. A comparative evaluation with various fault conditions and their severity is carried out by spectral analysis, and unique slip-dependent frequency components are identified in the spectra of diagnostic signals. This fault characterization is the most significant contribution of this paper.

Motor Current Signature Analysis-based Non-invasive Recognition of Mixed Eccentricity Fault in Line Start Permanent Magnet Synchronous Motor

— In this study, a cost-effective and non-invasive detection strategy is proposed for three-phase line start permanent magnet synchronous motor (LSPMSM) under mixed eccentricity fault using motor current signature analysis. In this respect, theory of air gap magnetic field in eccentric LSPMSM is presented. The detection strategy is examined through modeling and experimental studies. Two-dimensional time stepping finite element method is employed to calculate the motor parameters. Effects of mechanical load and fault severity on eccentric LSPMSM are also investigated. Different fault-related components are scrutinized and the most effective features are identified for this new type of electrical motor. The results indicate that amplitudes of fault-related components at rotor frequency are precise for early detection of mixed eccentricity in LSPMSM. Finally, an efficient frequency pattern as well as detection criterion is specified.

Fault Diagnosis in the Slip-Frequency Plane of Induction Machines Working in Time-Varying Conditions.

Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms—as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform—which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines.

Induction machines (IMs) are critical components of many industrial processes, what justifies the use of condition-based maintenance (CBM) systems for detecting their faults at an early stage, in order to avoid costly breakdowns of production lines. The development of CBM systems for IMs relies on the use of fast models that can accurately simulate the machine in faulty conditions. In particular, IM models must be able to reproduce the characteristic harmonics that the IM faults impress in the spatial waves of the air gap magneto-motive force (MMF), due to the complex interactions between spatial and time harmonics. A common type of fault is the eccentricity of the rotor core, which provokes an unbalanced magnetic pull, and can lead to destructive rotor-stator rub. Models developed using the finite element method (FEM) can achieve the required accuracy, but their high computational costs hinder their use in online CBM systems. Analytical models are much faster, but they need an inductance matrix that takes into account the asymmetries generated by the eccentricity fault. Building the inductance matrix for eccentric IMs using traditional techniques, such as the winding function approach (WFA), is a highly complex task, because these functions depend on the combined effect of the winding layout and of the air gap asymmetry. In this paper, a novel method for the fast and simple computation of the inductance matrix for eccentric IMs is presented, which decouples the influence of the air gap asymmetry and of the winding configuration using two independent tensors. It is based on the construction of a primitive inductance tensor, which formulates the eccentricity fault using single conductors as the simplest reference frame; and a winding tensor that converts it into the inductance matrix of a particular machine, taking into account the configuration of the windings. The proposed approach applies routine procedures from tensor algebra for performing such transformation in a simple way. It is theoretically explained and experimentally validated with a commercial induction motor with a mixed eccentricity fault.

A Simplified Dynamical Model of Mixed Eccentricity Fault in a Three-Phase Induction Motor

Dynamical model of a three-phase induction motor helps in understanding the physical behavior of the motor system. In a healthy motor, the air gap between the stator and rotor phases remains constant. However, due to eccentricity related faults, this air gap is subjected to a harmonic variation. To study the effects of such eccentricity related faults, modified winding function theory is applied to calculate the inductance values of stator and rotor phases. The computational complexity of the model increases when the rotor bars come into picture since the number of inductance values to be calculated increases at every time step. This article proposes a simplified technique to reduce this computational effort. Instead of considering the rotor bars, the circumference of the rotor periphery is divided into three phases. A suitable turns function that explains the physical phenomenon of eccentricity has been used. The effect of rotor bars has been taken into account in the turns function. Also, saturation effects that arise due to different load conditions have been considered. Accordingly, a closed-form expression for inductances has been derived. Magnetic coupled circuit approach has been implemented to develop the dynamical model of the motor system. The proposed model has been validated by finite element method and experimental results.

Non-Intrusive Methods to Detect Air-Gap Eccentricity Faults in Three-Phase Induction Motor

The focus of this paper is model-based behavioural analysis for the diagnosis of the air-gap eccentricity fault condition in the three-phase induction machine. To facilitate reliable and sensitive diagnosis, an online condition monitoring system has to depend on multiple signals and signatures. Air-gap mixed eccentricity fault signatures derived from three non-invasive methods such as stator line current, instantaneous power, and estimated air-gap torque are presented. Modified winding function theory and multiple coupled circuit approaches are used to model the machine. The sidebands present around the fundamental frequency in the spectrum of stator current, and around double the supply frequency in the spectrum of instantaneous power indicate the presence of air-gap mixed eccentricity. The low frequencies which exist near the DC component in the spectra of the air-gap torque and instantaneous power also indicate the same. These specific components were also observed in the high-frequency spectra around the principal slot harmonics. The modelling approach, the torque estimation approach, the simulation results at different severity and load conditions, and the experimentation result in a motor with prefabricated eccentricity are presented. Copyright © 2020 Praise Worthy Prize S.r.l. - All rights reserved.

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.

A Modified Direct-Quadrature Axis Model for Characterization of Air-gap Mixed Eccentricity Faults in Three-Phase Induction Motor

Advanced signal processing techniques and high-speed analog to digital converters enabled on-line detection of internal faults of induction motor even at inception. Reliable and accurate identification of fault signatures in practical situations is always a challenge due to load oscillations, supply harmonics or the presence of multiple faults. Hence model-based analyses are essential for diagnostic studies of faults in machines. This paper proposes a modified direct and quadrature (d-q) axis based approach for modeling a three-phase squirrel cage induction motor with air-gap mixed eccentricity faults. In the proposed model, air-gap length- and thus magnetizing reactance- are modeled as a rotor position-dependent function, to represent various variable air-gap fault conditions. Stator current spectrum is used as the diagnostic signal for detection of the presence of these faults. This simple approach of modeling is computationally less intensive compared to alternative approaches such as multiple coupled circuit modeling and finite element approach. Characteristic signatures of mixed eccentricity fault obtained by simulation studies were also validated in the motor current spectrum obtained through experimentation on a motor with prefabricated eccentricity.

Analytical Technique for Analysis and Detection of Eccentricity Fault in Surface-Mounted Permanent Magnet Generators Using No-Load Voltage Signature

This paper presents a method for modeling the effects of static eccentricity, dynamic eccentricity, and mixed eccentricity faults on the electromotive force (emf) of the surface-mounted permanent magnet generators (SMPMGs). The model is based on the combination of conformal mapping method (complex relative permeance) and eccentricity relative air gap permeance and it presents explicit formulas for no-load magnetic field distribution, magnetic flux, and emf in SMPMGs under different eccentricity fault types, some have not been so far considered elsewhere. The analytical and also numerical results show that there are some side-band components in the emf signature which can be used as eccentricity fault index. Analytical results validate by time-stepping finite element method (TSFEM) results.

Detection of mixed eccentricity fault in doubly‐fed induction generator based on reactive power spectrum

Wide application of wind energy in the world towards reduction of global warming, have made the low cost maintenance of these turbines very important. Basically, mechanical faults such as gear-box faults in these turbines lead to a long down time and consequently heavy financial loss. Generally, these faults cause eccentricity in the generator air gap, so by its early detection the fault can be largely prevented. In this paper, eccentricity fault detection is studied in doubly-fed induction generator (DFIG) which is the most established generator in wind turbines and also is more prone to mechanical faults compared to other technologies. One of frequency components of reactive power is introduced as eccentricity fault index. As the reactive power is always evaluated in the control system, there is no need to include a new hardware for fault detection. Finite elements method is used to model the wound rotor induction machine (WRIM) which is coupled with closed-loop control system. Following the validation of the model in the healthy case experimentally, eccentricity under different operating conditions is included in the modeling. Finally, a simple procedure based on simple signal processing methods is introduced for eccentricity fault diagnosis considering the operating conditions of the wind turbine.

The Use of a Multilabel Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults Based on the Start-Up Transient

In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three “problem transformation” methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time–frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time–frequency transformation.

Static air-gap eccentricity fault diagnosis using rotor slot harmonics in line neutral voltage of three-phase squirrel cage induction motor

The mixed eccentricity fault detection in a squirrel cage induction motor has been thoroughly investigated. However, a few papers have been related to pure static eccentricity fault and the authors focused on the RSH harmonics presented in stator current. The main objective of this paper is to present an alternative method based on the analysis of line neutral voltage taking place between the supply and the stator neutrals in order to detect air-gap static eccentricity, and to highlight the classification of all RSH harmonics in line neutral voltage. The model of squirrel cage induction machine relies on the rotor geometry and winding layout. Such developed model is used to analyze the impact of the pure static air-gap eccentricity by predicting the related frequencies in the line neutral voltage spectrum. The results show that the line neutral voltage spectrum are more sensitive to the air-gap static eccentricity fault compared to stator current one. The theoretical analysis and simulated results are confirmed by experiments.

Mixed Eccentricity Fault Diagnosis in Saturated Squirrel Cage Induction Motor using Instantaneous Power Spectrum Analysis

Mixed Eccentricity Fault Diagnosis in Saturated Squirrel Cage Induction Motor using Instantaneous Power Spectrum Analysis