scholarly journals Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 851-856 ◽  
Author(s):  
Wojciech Pietrowski ◽  
Konrad Górny
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Short Circuit ◽  
Induction Machine ◽  
General Regression Neural Network ◽  
Stator Winding ◽  
Element Analysis ◽  
The Neural Network ◽  
Start Up ◽  
Torque Analysis

AbstractRecently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred.

Detection of time-varying inter-turn short-circuit in a squirrel cage induction machine by means of generalized regression neural network

2017 ◽  
Vol 36 (1) ◽  
pp. 289-297 ◽  
Author(s):  
Wojciech Pietrowski
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Multilayer Perceptron ◽  
Short Circuit ◽  
Electrical Machines ◽  
General Regression Neural Network ◽  
Stator Winding ◽  
Time Varying ◽  
Content Type ◽  
Phase Current

Purpose Diagnostics of electrical machines is a very important task. The purpose of this paper is the presentation of coupling three numerical techniques, a finite element analysis, a signal analysis and an artificial neural network, in diagnostics of electrical machines. The study focused on detection of a time-varying inter-turn short-circuit in a stator winding of induction motor. Design/methodology/approach A finite element method is widely used for the calculation of phase current waveforms of induction machines. In the presented results, a time-varying inter-turn short-circuit of stator winding has been taken into account in the elaborated field-circuit model of machine. One of the time-varying short-circuit symptoms is a time-varying resistance of shorted circuit and consequently the waveform of phase current. A general regression neural network (GRNN) has been elaborated to find a number of shorted turns on the basis of fast Fourier transform (FFT) of phase current. The input vector of GRNN has been built on the basis of the FFT of phase current waveform. The output vector has been built upon the values of resistance of shorted circuit for respective values of shorted turns. The performance of the GRNN was compared with that of the multilayer perceptron neural network. Findings The GRNN can contribute to better detection of the time-varying inter-turn short-circuit in stator winding than the multilayer perceptron neural network. Originality/value It is argued that the proposed method based on FFT of phase current and GRNN is capable to detect a time-varying inter-turn short-circuit. The GRNN can be used in a health monitoring system as an inference module.

Diagnosis of Stator Winding Inter-Turn Circuit Faults in Induction Motors Based on Wavelet Packet Analysis and Neural Network

2012 ◽  
Vol 529 ◽  
pp. 37-42 ◽  
Author(s):  
Jun Yong Sang ◽  
Chen Hao ◽  
Peng Chao Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Short Circuit ◽  
Stator Winding ◽  
Wavelet Packet Analysis ◽  
Current Frequency ◽  
Frequency Band Energy ◽  
Short Circuit Fault

Aiming at the problem of the traditional stator current frequency spectrum analysis method cannot completely guarantee the accurate identification of stator winding inter-turn faults,the diagnosis method of stator winding inter-turn based on wavelet packet analysis (WPA) and Back Propagation (BP) neural network is put forward. The finite element model of the three-phase asynchronous motor which is based on Magnet is established, and analysis the magnetic flux density and current of the motor through simulation in normal and in the situation of short-circuit fault of stator winding inter-turn, the current signal of stator is analysised by wavelet packet , and the feature vector of frequency band energy is extracted as the basis to judge the state of induction motor running, and the motor state is identified by BP neural network, and the mapping from feature vector to the motor state is established. Simulation results show that: The diagnosis system of inter-turn fault based on WPA and BP neural network can effectively identify short-circuit fault between ratios. This is to say that the method has a high fault diagnosis rate.

Linear Static Response of Suspension Arm Based on Artificial Neural Network Technique

2011 ◽  
Vol 213 ◽  
pp. 419-426
Author(s):  
M.M. Rahman ◽  
Hemin M. Mohyaldeen ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Mesh Size ◽  
Maximum Displacement ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis

Modeling and simulation are indispensable when dealing with complex engineering systems. This study deals with intelligent techniques modeling for linear response of suspension arm. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique is used to predict the response of suspension arm. The linear static analysis was performed utilizing the finite element analysis code. The neural network model has 3 inputs representing the load, mesh size and material while 4 output representing the maximum displacement, maximum Principal stress, von Mises and Tresca. Finally, regression analysis between finite element results and values predicted by the neural network model was made. It can be seen that the RBFNN proposed approach was found to be highly effective with least error in identification of stress-displacement of suspension arm. Simulated results show that RBF can be very successively used for reduction of the effort and time required to predict the stress-displacement response of suspension arm as FE methods usually deal with only a single problem for each run.

The Novel Method of Structural Health Monitoring Using FEM and Neural Networks

2005 ◽  
Vol 475-479 ◽  
pp. 2099-2102 ◽  
Author(s):  
Shijie Zheng ◽  
Hong Tao Wang ◽  
Lifeng Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Beam ◽  
Epoxy Composite ◽  
The Novel ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis ◽  
Novel Method ◽  
Modal Values

In this paper, a new method of combining computational mechanics and neural networks for prediction of composite beam delamination is proposed. One beam with delamination, as well as a ‘healthy’ beam with no delamination, had a four-ply symmetric carbon/epoxy composite design, were fabricated simultaneously. The delamination was assumed at different location of the beam, and then the finite element analysis was performed and the modal frequencies of the composite beam were obtained, which were used to train the neural network. The piezoelectric patch was attached to the top of the composite beam to measure its modal frequencies. A feedforward backpropagation neural network was designed, trained, and used to predict the delamination location using the experimental modal values as inputs. The experimental results demonstrate that the predicted delamination location and size error is small.

Fabric Weave Pattern’s Recognition Based on Texture Orientation Features

2013 ◽  
Vol 423-426 ◽  
pp. 2404-2408 ◽  
Author(s):  
Jian Qiang Shen ◽  
Ge Ge Li ◽  
Xuan Zou ◽  
Yan Li
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Image Decomposition ◽  
Automatic Recognition ◽  
Wavelet Packet Transform ◽  
Original Image ◽  
Energy Distributions ◽  
Novel Approach ◽  
The Neural Network ◽  
Texture Orientation

A novel approach is proposed for representing fabric texture orientations and recognizing weave patterns. Wavelet packet transform is suited for fabric image decomposition in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet packet transform, and the regular orientations can be represented as the energy distributions of these images because the average energies of different fabric texture directions are changeable in a certain way. These energy orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify some complex weaves.

Diagnosis Technique Based on BP and D-S Theory

2011 ◽  
Vol 179-180 ◽  
pp. 544-548
Author(s):  
Qiu Yun Mo ◽  
Jie Cao ◽  
Feng Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
The Neural Network ◽  
Evidential Theory ◽  
The Given ◽  
Fusion Framework ◽  
Level Fusion ◽  
Theory And Experiments

This paper constructs a common data fusion framework of fault diagnosis, by combining local neural networks with dempster-shafer (D-S) evidential theory. The RBF neural network is proposed as a local neural network of the fault pattern recognition, and its input vectors are extracted by the wavelet packet decomposition of various frequency energy. Then, the signal of each sensor separately has a feature level fusion. This method is effective, verified by experiments. The given decision level fusion is based on combining the features of the neural network and the D-S theory, and experiments show the results of the fault diagnosis are more accurate by this method.

Detection and Analysis of Stator Winding Inter-Turn Short Circuit Fault in Permanent Magnet Linear Synchronous Motor

2012 ◽  
Vol 529 ◽  
pp. 322-326
Author(s):  
Cai Xia Gao ◽  
Chen Hao ◽  
Yue Bing Zhao
Keyword(s):  
Finite Element ◽  
Permanent Magnet ◽  
Short Circuit ◽  
Element Model ◽  
Synchronous Motor ◽  
Stator Winding ◽  
Element Analysis ◽  
Harmonic Content ◽  
Linear Synchronous Motor ◽  
Short Circuit Fault

A two-dimensional finite element model of PMLSM is build based on the finite element analysis software Magnet to research the diagnosis of stator winding inter-turn short circuit fault in PMLSM. The velocity, thrust, the stator current performance curve are obtained by simulation using Magnet when PMLSM is normal and under different extent inter-turn short circuit fault, the harmonic content of speed and thrust are analyzed using Matlab / Simulink , the conclusion that the thrust of the harmonic content is used as the Permanent Magnet Linear Synchronous Motor (PMLSM) stator inter-turn short circuit fault feature is proposed , which provided a basis for detection of stator winding inter-turn short circuit fault in PMLSM.

Modeling and Simulation of Induction Motor based on Finite Element Analysis

2016 ◽  
Vol 7 (4) ◽  
pp. 1100
Author(s):  
Yemna Bensalem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Modeling And Simulation ◽  
Induction Motor ◽  
Short Circuit ◽  
Induction Machine ◽  
Coupled Model ◽  
Simulation Platform ◽  
Element Analysis ◽  
Pwm Inverter

<p>This paper presents the development of a co-simulation platform of induction motor (IM). For the simulation, a coupled model is introduced which contains the control, the power electronics and also the induction machine. Each of these components is simulated in different software environments. So, this study provides an advanced modeling and simulation tools for IM which integrate all the components into one common simulation platform environment. In this work, the IM is created using Ansys-Maxwell based on Finite Element Analysis (FEA), whereas the power electronic converter is developed in Ansys-Simplorer and the control scheme is build in MATLAB-Simulink environment. Such structure can be useful for accurate design and allows coupling analysis for more realistic simulation. This platform is exploited to analyze the system models with faults caused by failures of different drive’s components. Here, two studies cases are presented: the first is the effects of a faulty device of the PWM inverter, and the second case is the influence of the short circuit of two stator phases. In order to study the performance of the control drive of the IM under fault conditions, a co-simulation of the global dynamic model has been proposed to analyze the IM behavior and control drives. In this work, the co-simulation has been performed; furthermore the simulation results of scalar control allowed verifying the precision of the proposed FEM platform.</p>

Short Circuit between Turns in Stator Winding of Induction Machine Fault Detection and Diagnosis

2013 ◽  
Vol 416-417 ◽  
pp. 565-571 ◽  
Author(s):  
Youcef Soufi ◽  
Tahar Bahi ◽  
H. Merabet ◽  
S. Lekhchine
Keyword(s):  
Induction Motor ◽  
Low Cost ◽  
Short Circuit ◽  
Induction Machine ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Stator Winding ◽  
Detection And Diagnosis ◽  
Abnormal Operating Conditions ◽  
Short Circuit Fault

The induction motor is one of the most used electric machines in variable speed system in the different field of industry due to its robustness, mechanical strength and low cost. Despite these qualities, the induction machine is subjected during its operation to a number of constraints of various natures (electrical, mechanical and environmental). This paper focuses on the diagnosis and the detection of the short circuit fault between turns in the stator winding of an induction machine, based on analyzing the evolution of the stator current in each stator phase, using tools based both on motor current spectral analysis and Park vector approach. A study by simulation was presented. The obtained results show that the considered methods can effectively diagnose and detect abnormal operating conditions in induction motor applications. Therefore, they clearly show the possibility of extracting signatures and the application of these techniques offered reliable and satisfactory results for the diagnosis and detection of such fault.

Sign in / Sign up

Export Citation Format

Share Document