scholarly journals Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

2017 ◽  
Vol 62 (4) ◽  
pp. 2413-2419 ◽  
Author(s):  
A. Glowacz ◽  
W. Glowacz ◽  
Z. Glowacz ◽  
J. Kozik ◽  
M. Gutten ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Induction Motors ◽  
Vibration Signal ◽  
Degradation Process ◽  
Conveyor Belt ◽  
Metallurgical Equipment ◽  
Financial Loss ◽  
Linear Discriminant ◽  
Three Phase

AbstractA degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies – Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

2019 ◽  
Vol 19 (6) ◽  
pp. 241-249 ◽  
Author(s):  
Adam Glowacz ◽  
Witold Glowacz ◽  
Jarosław Kozik ◽  
Krzysztof Piech ◽  
Miroslav Gutten ◽  
...  
Keyword(s):  
Induction Motor ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Diagnostic Techniques ◽  
Support Vector ◽  
Vibration Signals ◽  
Linear Discriminant ◽  
Three Phase ◽  
Vector Sum

Abstract Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %.

scholarly journals Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

2016 ◽  
Vol 61 (1) ◽  
pp. 153-158 ◽  
Author(s):  
A. Glowacz
Keyword(s):  
Induction Motor ◽  
Condition Monitoring ◽  
Induction Motors ◽  
Correct Diagnosis ◽  
Acoustic Signals ◽  
Metallurgical Equipment ◽  
Financial Loss ◽  
Data Processing Method ◽  
Electrical Faults ◽  
Metallurgical Processes

Condition monitoring of deterioration in the metallurgical equipment is essential for faultless operation of the metallurgical processes. These processes use various metallurgical equipment, such as induction motors or industrial furnaces. These devices operate continuously. Correct diagnosis and early detection of incipient faults allow to avoid accidents and help reducing financial loss. This paper deals with monitoring of rotor electrical faults of induction motor. A technique of recognition of acoustic signals of induction motors is presented. Three states of induction motor were analyzed. Studies were carried out for methods of data processing: Method of Selection of Amplitudes of Frequencies (MSAF10) and Bayes classifier. Condition monitoring is helpful to protect induction motors and metallurgical equipment. Further researches will allow to analyze other metallurgical equipment.

Recent Techniques to Identify the Stator Fault Diagnosis in Three Phase Induction Motor

2016 ◽  
pp. 309-325
Author(s):  
K. Vinoth Kumar ◽  
S. Suresh Kumar ◽  
A. Immanuel Selvakumar ◽  
R. Saravana Kumar
Keyword(s):  
Fault Diagnosis ◽  
Life Span ◽  
Induction Motor ◽  
Induction Motors ◽  
Early Stage ◽  
Decomposition Technique ◽  
Maintenance Schedule ◽  
Three Phase ◽  
Stator Fault ◽  
Financial Losses

Induction motors have gained its popularity as most suitable industrial workhorse, due to its ruggedness and reliability. With the passage of time, these workhorses are susceptible to faults, some are incipient and some are major. Such fault can be catastrophic, if unattended and may develop serious problem that may lead to shut down the machine causing production and financial losses. To avoid such breakdown, an early stage prognosis can help in preparing the maintenance schedule, which will lead to improve its life span. Scientist and engineers worked with different scheme to diagnose the machine faults. In this paper, the authors diagnose the turn-to-turn faults condition of the stator through symmetrical component analysis. The results of the analysis also verified through Power Decomposition Technique (PDT) in Matlab /SIMULINK. The results are compatible with the published results for known faults.

scholarly journals Fault Diagnosis of Induction Motor Using Convolutional Neural Network

2019 ◽  
Vol 9 (15) ◽  
pp. 2950 ◽  
Author(s):  
Jong-Hyun Lee ◽  
Jae-Hyung Pack ◽  
In-Soo Lee
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Induction Motor ◽  
Induction Motors ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Industrial Equipment ◽  
Diagnosis System ◽  
Fault Diagnosis System

Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.

scholarly journals Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

2020 ◽  
Vol 11 (1) ◽  
pp. 314
Author(s):  
Gustavo Henrique Bazan ◽  
Alessandro Goedtel ◽  
Marcelo Favoretto Castoldi ◽  
Wagner Fontes Godoy ◽  
Oscar Duque-Perez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Operating Conditions ◽  
Industrial Systems ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Three Phase ◽  
C4.5 Decision Tree ◽  
The Time Domain

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.

scholarly journals Windings Design for Single-phase Induction Motors Base on 4-phase Induction Motor (Case study: identical windings design)

2018 ◽  
Vol 215 ◽  
pp. 01023 ◽  
Author(s):  
Zuriman Anthony ◽  
Erhaneli Erhaneli ◽  
Zulkarnaini Zulkarnaini
Keyword(s):  
Induction Motor ◽  
Power Factor ◽  
Single Phase ◽  
Induction Motors ◽  
Three Phase ◽  
Motor Design

A 1-phase induction motor usually has a complicated windings design which compares to polyphase induction motor. In addition, a large capacitor start is required to operate the motor. It is an expensive way to operate the motor if it compare to polyphase induction motor. So, a new innovation method is required to make the motor more simple and cheaper. This research is purposed to study a new winding design for a single-phase capacitor motor. Winding design of the motor was conducted to a simple winding design like a 4-phase induction motor that has four identical windings. The comparator motor that use in this study was a Three-phase induction motor with data 1400 RPM, 1.5 HP, 50Hz, 380/220V, Y/Δ, 2.74/4.7A, 4 poles, that had the same current rating which the proposed method. The result showed that the motor design on this proposed method could be operated at 88.18 % power rating with power factor close to unity.

scholarly journals Experimental Study of Various Parameters during Speed Control of Three-phase Induction Motor Using GPIC and LabVIEW

2021 ◽  
Vol 5 (1) ◽  
pp. 51-62
Author(s):  
Adnan Ahmed ◽  
Abdul Majeed Shaikh ◽  
Muhammad Fawad Shaikh ◽  
Shoaib Ahmed Shaikh ◽  
Jahangir Badar Soomro
Keyword(s):  
Induction Motor ◽  
Induction Motors ◽  
Induction Machine ◽  
Speed Control ◽  
Industrial Applications ◽  
General Purpose ◽  
Three Phase ◽  
Field Programmable ◽  
Input Source ◽  
Open Control

Induction motors are widely used from home to industrial applications. Speed of induction motor plays important role, so to control the speed of induction motor various techniques are adopted and one of these techniques is V/F control, which is adopted in this paper. This technique helps to control the speed in open control system in RPM. Moreover, Control is designed in LabVIEW, it is quite helpful to develop the circuit graphically and code is automatically written in the background to run on Field Programmable Gate Array (FPGA). The aim of this research is to study the impacts on diverse parameters during speed control of three phase induction machine with manipulation of GPIC. Solar technology is used as input source to drive the General-Purpose Inverter Controller (GPIC). Apart of this, impacts of modulation index and carrier frequency influencing the active, reactive and apparent power, temperature and power quality and current overshoot is analysed. MATLAB/Simulink and LabVIEW tools are used for simulation and results along with GPIC, Induction motor and solar panel as hardware.

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