scholarly journals Studying and Simulating the Influence of the Rotor Fault on Stator Current

2020 ◽  
Vol 17 (1) ◽  
pp. 17-21
Author(s):  
Katalin Ágoston
Keyword(s):  
Equivalent Circuit ◽  
Frequency Spectrum ◽  
Induction Motors ◽  
Signature Analysis ◽  
Stator Current ◽  
Motor Current ◽  
Squirrel Cage ◽  
Motor Current Signature Analysis ◽  
Cage Rotor ◽  
Current Signature

AbstractThis paper presents fault detection techniques, especially the motor current signature analysis (MCSA) which consists of the phase current measurement of the electrical motor’s stator and/or rotor. The motor current signature analysis consists in determining the frequency spectrum (FFT) of the stator current signal and evaluating the relative amplitude of the current harmonics. Sideband frequencies appear in the frequency spectrum of the current, corresponding to each fault. The broken bar is a frequent fault in induction motors with squirrel-cage rotor. It is presented the equivalent circuit for induction motors and the equivalence between the squirrel-cage rotor and the rotor windings. It is also presented an equivalent circuit model for induction motors with squirrel cage rotor, and based on this a Simulink model was developed. It is shown how a broken rotor bar influences the magnetic field around the rotor and through this the stator current. This modification is highlighted through the developed model.

scholarly journals Analysis of Induction Motor Performance Using Motor Current Signature Analysis Technique

2021 ◽  
Vol 5 (1) ◽  
pp. PRESS
Author(s):  
Ramadoni Syahputra ◽  
Hedi Purwanto ◽  
Rama Okta Wiyagi ◽  
Muhamad Yusvin Mustar ◽  
Indah Soesanti
Keyword(s):  
Power Plant ◽  
Induction Motor ◽  
Induction Motors ◽  
Signature Analysis ◽  
Steam Power ◽  
Reliable Technique ◽  
Steam Power Plant ◽  
Motor Current ◽  
Motor Current Signature Analysis ◽  
Current Signature

This paper discusses the analysis of the performance of an induction motor using the motor current signature analysis (MCSA) technique. Induction motor is a type of electric machine that is widely used in industry. One of the industries that utilize induction motors is a steam power plant (SPP). The role of induction motors is very vital in SPP operations. Therefore, it is necessary to monitor the performance, stability, and efficiency to anticipate disturbances that can cause damage or decrease the life of the induction motor. MCSA is a reliable technique that can be used to analyze damage to an induction motor. In this technique, the induction motor current signal is detected using a current transducer. The signal is then passed on to the signal conditioning and then into the data acquisition device. The important signal data is analyzed in adequate computer equipment. The results of this analysis determine the condition of the induction motor, whether it is normal or damaged. In this research, a case study was carried out at the Rembang steam power plant, Central Java, Indonesia. The results of the analysis of several induction motors show that most of them are in normal conditions and are still feasible to operate.

A Cost-Effective Computerized Data Acquisition and Motor Current Signature Analysis Demonstrator for Industry and Academia

2016 ◽  
Author(s):  
Adrian Georgescu ◽  
P. A. Simionescu
Keyword(s):  
Data Acquisition ◽  
Induction Motors ◽  
Test Procedure ◽  
Cost Effective ◽  
Fault Identification ◽  
Signature Analysis ◽  
Laboratory Exercise ◽  
Motor Current ◽  
Motor Current Signature Analysis ◽  
Current Signature

This paper presents the development, results and trainee perception of a laboratory experiment used for diagnosing the occurrence of different faults in impeller-pump induction motors by means of the Motor Current Signature Analysis (MCSA) technique. This is a quintessential experiment, relatively inexpensive and easy to implement, that combines elements of computerized data acquisition, Discrete Fourier Transform analysis and fault identification of electric motors. Following this laboratory exercise, students and trainees are able to understand and apply MCSA to determine common faults of induction motors. The test stand, experimental setup, and test procedure are described with sufficient details in the paper for others to build one of their own.

scholarly journals Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1469
Author(s):  
Tomas A. Garcia-Calva ◽  
Daniel Morinigo-Sotelo ◽  
Vanessa Fernandez-Cavero ◽  
Arturo Garcia-Perez ◽  
Rene de J. Romero-Troncoso
Keyword(s):  
Induction Motors ◽  
Electrical Machines ◽  
Signature Analysis ◽  
Time Frequency ◽  
Stator Current ◽  
Analysis Methodology ◽  
Fault Severity ◽  
Motor Current Signature Analysis ◽  
Stationary Conditions ◽  
Current Signature

The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks.

scholarly journals Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3721 ◽  
Author(s):  
Martin Valtierra-Rodriguez ◽  
Jesus R. Rivera-Guillen ◽  
Jesus A. Basurto-Hurtado ◽  
J. Jesus De-Santiago-Perez ◽  
David Granados-Lieberman ◽  
...  
Keyword(s):  
Induction Motors ◽  
Transient State ◽  
Operating Conditions ◽  
Signature Analysis ◽  
Broken Rotor Bar ◽  
Time Frequency ◽  
Broken Rotor Bars ◽  
Motor Current ◽  
Motor Current Signature Analysis ◽  
Current Signature

Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.

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