Stator Faults In Induction Motors Research Articles

Online Diagnosis and Discrimination of Stator Faults in Six-Phase Induction Motors Based on Voltage Symmetrical Components

In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines, mainly due to their ability to operate smoothly under fault conditions. Due to multiple stator phase arrangements, existing fault diagnostic (FD) methods are not sufficiently universal and cannot be applied to the multiple configurations of multiphase machines. In this regard, the current research proposes a universal and effective online diagnostics technique based on the computation and monitoring of a relevant severity factor, which is defined as the ratio of the zero, negative and positive voltage symmetrical components. The Short Time Least Square Prony’s (STLSP) approach is used to implement this method online in LabVIEW environment. In addition to the wide applicability of the method, the obviation of any motor parameter estimation and limited requirements regarding variables measurements make the proposed approach extremely advantageous for motors with high number of phases. This article addresses at stator faults in symmetrical six-phase induction machines (6PIM). The proposed method’s experimental results on six-phase motor drive platforms demonstrate the excellent generalization capability of the proposed method, as well as high accuracy and robustness, along with the ability to accurately distinguish between the occurrence of Inter-Turn Short Circuit (ITSC) faults and the presence of Unbalance Supply Voltage (USV) conditions. A 6PIM is put through its paces in both healthy and faulty conditions. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults and USV operating conditions with high reliability, fastness and high precision.

Hybrid Model-Based Fuzzy Logic Diagnostic System for Stator Faults in Three-Phase Cage Induction Motors

Hybrid Model-Based Fuzzy Logic Diagnostic System for Stator Faults in Three-Phase Cage Induction Motors

Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

The detection results need to be analyzed and distinguished by professional technicians in the fault detection methods for induction motors based on signal processing and it is difficult to realize the automatic identification of stator and rotor faults. A method for identifying stator and rotor faults of induction motors based on machine vision is proposed to solve this problem. Firstly, Park’s vector approach (PVA) is used to analyze the three-phase currents of the motor to obtain Park’s vector ring (PVR). Then, the local binary patterns (LBP) and gray level cooccurrence matrix (GLCM) are combined to extract the image features of PVR. Finally, the vectors of image features are used as input and the types of induction motor faults are identified with the help of a random forest (RF) classifier. The proposed method has achieved high identification accuracy in both the Maxwell simulation experiment and the actual motor experiment, which are 100% and 95.83%, respectively.

Induction Motor Stator Fault Classification Using PCA-ANFIS Technique

Induction motors are used commonly for industrial operations due to their ease of operation coupled with ruggedness and reliability. However, they are subjected to stator faults which result in damage and consequently revenue losses. The classification of stator fault in a three-phase induction motor based on Adaptive neuro-fuzzy inference system (ANFIS) in combination with Principal Component Analysis (PCA) is proposed in this study. A burnt motor was redesigned and rewound while data acquisition was developed to acquire the current and vibration data needed for the fault classification. The data feature extraction for the fault classification was carried out by PCA while backpropagation and the least-squares algorithms were used for the training of the data. Three principal components, which severs as input for the ANFIS, were used to represent the entire data. The ANFIS was tested under four different paradigms, while the membership function type and epoch number were changed at each instant. The ANFIS model based on the triangular membership function and 10 epoch number was found appropriate and used, bringing the accuracy of the model to over 99% with the lowest ANFIS training RMSE error of 1.1795e-6. The ANFIS validation results of the fault classification show that the results are accurate, indicating that the PCA-ANFIS technique is applicable in fault diagnosis and classification of stator faults in induction motors.

Generalized Likelihood Ratio Test Based Approach for Stator-Fault Detection in a PWM Inverter-Fed Induction Motor Drive

This paper proposes a new approach for the detection of stator faults in inverter-fed induction motor under closed-loop control. These faults, generally, start with interturn short circuit and can evolve to phase-to-phase or phase-to-ground faults, leading to stator currents unbalance. In the considered application, the stator windings are supplied by a static converter for the control of the speed and the rotor flux of the motor. The unbalance fault has been artificially created through additional resistance in series with one phase of the stator windings. The proposed diagnosis approach is based on the computation of the symmetrical components of the stator currents. The supply fundamental frequency and the three-phase phasors are estimated based on the maximum likelihood estimator. Then, the generalized likelihood ratio test is applied for unbalance fault detection. Simulation and experimental results on a 1.5-kW induction motor illustrate the effectiveness of the proposed approach, leading to an effective diagnosis procedure for stator faults in inverter-fed induction motor under steady state and closed-loop operation.

Detection of Inter-Turn Stator Faults in Induction Motors Using Short-Term Averaging of Forward and Backward Rotating Stator Current Phasors for Fast Prognostics

Detection of Inter-Turn Stator Faults in Induction Motors Using Short-Term Averaging of Forward and Backward Rotating Stator Current Phasors for Fast Prognostics

Induction Motor Stator Fault Diagnosis by Rotor Slots Harmonics Tracking Using Prony Improved Approach

The short-circuit fault diagnosis is essential in order to avoid irreversible damages that could affect the stator circuit of the induction motor. For this purpose, this paper addresses a new approach based on Prony's high resolution spectral analysis method to avoid the drawbacks of the well-known method based on the power spectral density estimation by Periodogram technique. This new approach, called Root-Prony, allows a better frequency resolution even on a very short acquisition time as well as a better detection even on very small magnitudes. Moreover, and in order to reduce the computation time of this approach, this paper proposes to analyze only the band around the first rotor slot frequencies. The choice of this band allows avoiding any confusion with the frequency signatures of external phenomena which appear most often in very low frequencies. The experimental results obtained show the efficiency and the reliability of this new approach in tracking the induction motor stator winding degradation.

Stator and Rotor Faults Monitoring of the Inverter-Fed Induction Motor Drive using State Estimators

The paper deals with the application of the Extended Kalman Filter and the Extended Luenberger Observer algorithms for the stator and rotor fault detection of the induction motor fed by PWM inverter. The induction motor conditions are analyzed using estimated rotor and stator-winding resistance. Mathematical models of the extended state estimators are presented, in which the stator and rotor resistances are added as additional electromagnetic state variables. Experimental results for the inverter-fed induction motor, with shorted stator-turns and broken rotor bars, are presented and analysed.

Wavelet Based Induction Motor Fault Diagnosis Using Zero Sequence Current

Induction motor stator fault is diagnosed by applying Discrete Wavelet transform on zero sequence components. The single phasing stator fault is created and diagnosed in the induction motor model developed in stationary reference frame, under varying load conditions. The stator inter-turn incipient fault is created and diagnosed in the induction motor experimental setup as well under no load condition. The qdo components are calculated from Park’s equations. The faults can be diagnosed from wavelet transform of the zero sequence current components. PSD is used for diagnosing the fault and the statistical value is used for verifying the result. The energy is calculated using Parseval’s theorem. The energy and the statistical data calculated from the wavelet coefficients of zero sequence current components are used as fault indicators. The energy value is able to reveal the fault severity in the induction motor stator winding. Power spectral Density along with Discrete Wavelet Transform plays very important role in diagnosing the fault.

Induction Motor Stator Fault Detection by a Condition Monitoring Scheme Based on Parameter Estimation Algorithms

This article presents a simple, low-cost, and effective method for the early diagnosis of stator short-circuit faults. The approach relies on the combination of an induction motor mathematical model and parameter estimation algorithm. The kernel of the method is the efficient search for the characteristic parameters that indicate stator short-circuit faults. However, the non-linearity of a machine model may imply multiple local minima of an objective function implemented in the estimation algorithm. Taking this into consideration, the suitability of two industry-proven optimization algorithms (pattern search algorithm and genetic algorithm) as applied in the proposed condition monitoring method was investigated. Experimental results show that the proposed diagnosis method is capable of detecting stator short-circuit faults and estimating level and location of faults. The study also indicates that the proposed method is robust to motor parameters offset and unbalanced voltage supply. Application of the pattern search algorithm is suitable for a continuous monitoring system, where the previous result can be used as starting point of the new search. The genetic algorithm requires longer computation time and is suitable for the offline diagnostic system. It is not sensitive to the starting point, and achieving global solution is guaranteed.

Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis

The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.

Online Fault Diagnosis of Three Phase Squirrel Cage Induction Motor Stator Electrical Faults using Current Parks Vector Approach and Motor Current Signature Analysis

Nowadays the online fault diagnosis technique is getting more attention in electrical manufacturing and processing industries. Because of its unique advantages and the fast response to identify faults. In such a way the anticipated process also deals with the talented practice called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The projected technique lies in the actuality that by using current park's vector approach, the inbuilt non-stationary character of current in the stator winding can be precisely considered. The Park's Vector approach can be used to detect the different types of motor's faults. The methods have an ability to distinguish the machine conditions like faulty and healthy one by the way of representing pattern, is the key characteristic of the projected technique. In the Park's Vector approach the results of the system are represented as a wonderful circle for undamaged machine and the elliptic representation is used to unbalance due to winding faults. Thus the process of comparing the patterns generated by the system the machine condition can detect easily. In this paper the different fault of the three phase squirrel cage induction motor typical faults is analyzed by patterns of Park's Vector.

A new and best approach for early detection of rotor and stator faults in induction motors coupled to variable loads

Today, induction machines are playing, thanks to their robustness, an important role in world industries. Although they are quite reliable, they have become the target of various types of defects. Thus, for a long time, many research laboratories have been focusing their works on the theme of diagnosis in order to find the most efficient technique to predict a fault in an early stage and to avoid an unplanned stopping in the chain of production and costs ensuing. In this paper, an approach called Park’s vector product approach (PVPA) was proposed which was endowed with a dominant sensitivity in the case in which there would be rotor or stator faults. To show its high sensitivity, it was compared with the classical methods such as motor current signature analysis (MCSA) and techniques studied in recent publications such as motor square current signature analysis (MSCSA), Park’s vector square modulus (PVSM) and Park-Hilbert (P-H) (PVSMP-H). The proposed technique was based on three main steps. First, the three-phase currents of the induction motor led to a Park’s vector. Secondly, the proposed PVPA was calculated to show the distinguishing spectral signatures of each default and specific frequencies. Finally, simulation and experimental results were presented to confirm the theoretical assumptions.

Feature extraction of induction motor stator fault based on particle swarm optimization and wavelet packet

To effectively extract the interturn short circuit fault features of induction motor from stator current signal, a novel feature extraction method based on the bare-bones particle swarm optimization (BBPSO) algorithm and wavelet packet was proposed. First, according to the maximum inner product between the current signal and the cosine basis functions, this method could precisely estimate the waveform parameters of the fundamental component using the powerful global search capability of the BBPSO, which can eliminate the fundamental component and not affect other harmonic components. Then, the harmonic components of residual current signal were decomposed to a series of frequency bands by wavelet packet to extract the interturn circuit fault features of the induction motor. Finally, the results of simulation and laboratory tests demonstrated the effectiveness of the proposed method.

An adaptive artificial immune system for fault classification

Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three steps. First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board. Then feature patterns are obtained to identify the fault using current signals. Second, the fault related features are extracted from three-phase currents. Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults. The proposed method was experimentally implemented on a 0.37 kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.

Diagnosis of stator faults in induction motor based on zero sequence voltage after switch-off

To improve the accuracy of the stator winding fault diagnosis in induction motor, a new diagnostic method based on the Hilbert-Huang transform (HHT) was proposed. The ratio of fundamental zero sequence voltage to positive sequence voltage after switch-off was selected as the stator fault characteristic, which could effectively avoid the influence of the supply unbalance and the load fluctuation, and directly represent the asymmetry in the stator. Using the empirical mode decomposition (EMD) based on HHT, the zero sequence voltage after switch-off was decomposed and the fundamental component was extracted. Then, the fault characteristic can be acquired. Experimental results on a 4-kW induction motor demonstrate the feasibility and effectiveness of this method.

Unsupervised Neural-Network-Based Algorithm for an On-Line Diagnosis of Three-Phase Induction Motor Stator Fault

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, an automatic algorithm based an unsupervised neural network for an on-line diagnostics of three-phase induction motor stator fault is presented. This algorithm uses the alfa-beta stator currents as input variables. Then, a fully automatic unsupervised method is applied in which a Hebbian-based unsupervised neural network is used to extract the principal components of the stator current data. These main directions are used to decide where the fault occurs and a relationship between the current components is calculated to verify the severity of the fault. One of the characteristics of this method, given its unsupervised nature, is that it does not need a prior identification of the system. The proposed methodology has been experimentally tested on a 1 kW induction motor. The obtained experimental results show the effectiveness of the proposed method. </para>

Multiple Reference Frames Theory: A New Method for the Diagnosis of Stator Faults in Three-Phase Induction Motors

This paper proposes the use of the multiple reference frames theory for the diagnosis of stator faults in three-phase induction motors. The development of a simplified mathematical motor model allowed the establishment of the equivalent circuits of the motor, in d-q-0 axes, in the presence of stator interturn short circuits. The use of the stationary reference frame, clockwise and counterclockwise synchronous reference frames, allows the extraction and manipulation of the information contained in the motor supply currents in a way that the effects introduced by the fault are easily isolated and measured. A severity factor is defined and the simulation and experimental results presented demonstrate its independence in relation to the working conditions of the motor, such as the load level and unbalances in the voltage supply system. Although the technique is here introduced for the diagnosis of stator faults, it is possible to extend its use for the diagnosis of other asymmetries such as broken rotor bars and air-gap eccentricity.

DSP Implementation of the Multiple Reference Frames Theory for the Diagnosis of Stator Faults in a DTC Induction Motor Drive

This paper deals with the use of a new diagnostic technique based on the multiple reference frames theory for the diagnosis of stator winding faults in a direct-torque-controlled (DTC) induction motor drive. The theoretical aspects underlying the use of this diagnostic technique are presented but a major emphasis is given to the integration of the diagnostic system into the digital-signal-processor (DSP) board containing the control algorithm. Taking advantage of the sensors already built in the drive for control purposes, it is possible to implement this diagnostic system at no additional cost, thus giving a surplus value to the drive itself. Experimental results show the effectiveness of the proposed technique to diagnose stator faults and demonstrate the possibility of its integration in the DSP board.

Diagnosis of Stator Inter-Turn Short Circuits in DTC Induction Motor Drives

This paper addresses the subject of stator fault diagnosis in direct torque control (DTC) induction motor drives. The particularities of the behavior of a DTC drive, when in the presence of inter-turn short circuits, are exploited, thus allowing the proposal of two different approaches for the diagnosis of this type of fault. One of the proposed diagnostic techniques is the well known motor current spectrum analysis. It is demonstrated that in a DTC induction motor drive, with stator inter-turn short circuits, the action of the torque and flux controllers introduce a strong third harmonic in the motor supply currents, which can be used to detect this type of fault. The other diagnostic technique is based on the multiple reference frames theory, recently proposed by the authors for the diagnosis of stator faults in line-connected motors. Here, its use is exploited in the diagnosis of stator faults in DTC induction motor drives. Simulation and experimental results demonstrate the effectiveness of the two proposed techniques for the diagnosis of stator faults but emphasize the fact that the multiple reference frames technique is the preferred one when the residual asymmetries of the motor are not negligible.