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.

GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.

Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown.Recently,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)faults.However,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the literature.Therefore,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and 1D-CNN.Initially,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature Analysis.The faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions.This is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction motor.By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL models.All the DL models are trained and validated with a suitable number of architecture layers.By simulation,the multiclass confusion matrix,precision,recall,and F1-score are obtained in several conditions.The result shows that the stator current signature of the motor can be used to detect individual and multiple faults.Moreover,deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature.In deep learning(DL)models,the LSTM has shown better accuracy among all other three models.These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.

Impact of Motor Stator Winding Faults on Motor Differential-mode Impedance and Mode Transformation

Motor impedance and mode transformation have significant effects on the electromagnetic interference(EMI)generated in motor drive systems.Stator winding faults commonly cause motor failure;however,in their early stages,they may not affect the short-term operation of the motor.To date,EMI noise under the influence of premature stator winding faults has not been adequately studied,particularly the differential-mode(DM)noise due to the common-mode(CM)-to-DM transformation.This study investigates and quantifies the influence of stator winding faults on the motor DM impedance and mode transformation.First,the transmission line model of an induction motor is described based on the scattering(S)parameter measurements of each phase of the motor.It offers the flexibility to emulate different types of stator winding faults at specific locations and various severities,such that the impacts of the faults on the motor DM impedance can be easily estimated.Second,a test setup is proposed to quantify the CM-to-DM transformation due to the stator winding faults.The findings of this study reveal that even the early stages of stator winding faults can result in significant changes in the DM noise.

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure according to statisti-cal studies.In this paper,an algorithm for the detection of an SITF is presented.It is based on one of the blind source separation techniques called principal component analysis(PCA).The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components.The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion.The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply.In addition,with a straightforward and low computational burden in the fault detection process,the proposed method is computationally efficient.To evaluate the performance of the proposed method,large numbers of practical and simulation scenarios are considered,and the results confrm the good performance,high degree of accuracy,and good convergence speed of the proposed method.

Condition monitoring of a wind turbine generator using a standalone wind turbine emulator

The intend of this paper is to give a description of the realization of a low-cost wind turbine emulator （WTE） with open source technology from graze required for the condition monitoring to diagnose rotor and stator faults in a wind turbine generator （WTG）. The WTE comprises of a 2.5 kW DC motor coupled with a 1 kW squirrel-cage induction machine. This paper provides a detailed overview of the hardware and software used along with the WTE control strategies such as MPPT and pitch control. The emulator reproduces dynamic characteristics both under step variations and arbitrary variation in the wind speed of a typical wind turbine （WT） of a wind energy conversion system （WECS）. The usefulness of the setup has been benchmarked with previously verified WT test rigs made at the University of Manchester and Durham University in UK. Considering the fact that the rotor blades and electric subassemblies direct drive WTs are most susceptible to damage in practice, generator winding faults and rotor unbalance have been introduced and investigated using the terminal voltage and generated current. This wind turbine emulator （WTE） can be reconfigured or analyzed for condition monitoring without the need for real WTs.