Signal demodulation is a fundamental procedure in many situations during a spectral analysis. Through an envelope analysis, it becomes possible to identify fault frequencies that are embedded in a modulated signal and that are not clearly visible only by directly applying some signal processing techniques such as the Fourier transform and filtering. In this paper, a very simple and empirical technique for demodulation is proposed. It is based only on the analysis of local extremes from a modulated time sequence to find a new time sequence that carries the wanted relevant fault data. The method of analysis is a good alternative tool for electrical fault detection in induction motors. The numerical and experimental results demonstrate the eff...
Abstract: A new method of automatic diagnosis of induction motor faults based on the time-frequency ...
Fault detection in squirrel cage induction machines based on stator current spectrum has been widely...
This video tutorial was prepared for the 13th Edition of the IEEE International Symposion on Diagno...
Signal demodulation is a fundamental procedure in many situations during a spectral analysis. Throug...
This paper introduces a novel time-domain method for detecting the four major types of induction mot...
Faults, such as broken rotor bars, in induction motors may be detected by estimating the spectral si...
This chapter introduces recent developments in fault diagnostics of induction motors (IMs), by provi...
Abstract: Today, in response to the industrial requirements, the diagnosis and monitoring of the ele...
Induction motors are critical components for most industries and the condition monitoring has become...
International audienceCondition monitoring of electrical machines is a broad scientific area, the ul...
Motor Current Signature Analysis is the reference method for the diagnosis of induction machines. Ho...
AbstractThis paper has developed a technique for extraction of Low frequency oscillations below 50 h...
Incipient fault detection of the induction machines (IM) prevents the unscheduled downtime and hence...
Fault detection in squirrel cage induction machines based on stator current spectrum has been widely...
The squirrel cage induction motor is the most common means of converting electrical energy to mecha...
Abstract: A new method of automatic diagnosis of induction motor faults based on the time-frequency ...
Fault detection in squirrel cage induction machines based on stator current spectrum has been widely...
This video tutorial was prepared for the 13th Edition of the IEEE International Symposion on Diagno...
Signal demodulation is a fundamental procedure in many situations during a spectral analysis. Throug...
This paper introduces a novel time-domain method for detecting the four major types of induction mot...
Faults, such as broken rotor bars, in induction motors may be detected by estimating the spectral si...
This chapter introduces recent developments in fault diagnostics of induction motors (IMs), by provi...
Abstract: Today, in response to the industrial requirements, the diagnosis and monitoring of the ele...
Induction motors are critical components for most industries and the condition monitoring has become...
International audienceCondition monitoring of electrical machines is a broad scientific area, the ul...
Motor Current Signature Analysis is the reference method for the diagnosis of induction machines. Ho...
AbstractThis paper has developed a technique for extraction of Low frequency oscillations below 50 h...
Incipient fault detection of the induction machines (IM) prevents the unscheduled downtime and hence...
Fault detection in squirrel cage induction machines based on stator current spectrum has been widely...
The squirrel cage induction motor is the most common means of converting electrical energy to mecha...
Abstract: A new method of automatic diagnosis of induction motor faults based on the time-frequency ...
Fault detection in squirrel cage induction machines based on stator current spectrum has been widely...
This video tutorial was prepared for the 13th Edition of the IEEE International Symposion on Diagno...