Condition monitoring and reliability of bearing units of induction machines

2016 ◽  
Author(s):  
Nail Safin ◽  
Vladimir Prakht ◽  
Vladimir Dmitrievskii
Keyword(s):  
Condition Monitoring ◽  
Induction Machines

Locally optimized chirplet spectrogram for condition monitoring of induction machines in transient regime

Measurement ◽  
2022 ◽  
pp. 110690
Author(s):  
J. Martinez-Roman ◽  
R. Puche-Panadero ◽  
A. Sapena-Bano ◽  
J. Burriel-Valencia ◽  
M. Riera-Guasp ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Induction Machines ◽  
Transient Regime

scholarly journals Detection of Stator and Rotor Asymmetries Faults in Wound Rotor Induction Machines: Modeling, Test and Real-Time Implementation

2021 ◽  
Author(s):  
Shahin Hedayati Kia
Keyword(s):  
Real Time ◽  
Condition Monitoring ◽  
Classical Model ◽  
Induction Machine ◽  
Induction Machines ◽  
Time Data ◽  
Jules Verne ◽  
Three Phase ◽  
Experimental Test Bench ◽  
Different Levels

This chapter deals with detection of stator and rotor asymmetries faults in wound rotor induction machines using rotor and stator currents signatures analysis. This is proposed as the experimental part of fault diagnosis in electrical machines course for master’s degree students in electrical engineering at University of Picardie “Jules Verne”. The aim is to demonstrate the main steps of real-time condition monitoring development for wound rotor induction machines. In this regard, the related parameters of classical model of wound rotor induction machine under study are initially estimated. Then, the latter model is validated through experiments in both healthy and faulty conditions at different levels of the load. Finally, an algorithm is implemented in a real-time data acquisition system for online detection of stator and rotor asymmetries faults. An experimental test bench based on a three-phase 90 W wound rotor induction machine and a real-time platform for hardware-in-the-loop test are utilized for validation of the proposed condition monitoring techniques.

scholarly journals Bearing fault detection by four-band wavelet packet decomposition

2019 ◽  
Vol 23 (Suppl. 1) ◽  
pp. 91-98
Author(s):  
Yalcin Cekic
Keyword(s):  
Computational Complexity ◽  
Production Process ◽  
Condition Monitoring ◽  
Wavelet Packet ◽  
Induction Machines ◽  
Wavelet Packet Decomposition ◽  
Vibration Data ◽  
Monitoring Applications ◽  
Motor Condition ◽  
Motor Vibration

Bearing problems are by far the biggest cause of induction motor failures in the industry. Since induction machines are used heavily by the industry, their unexpected failure may disturb the production process. Motor condition monitoring is employed widely to avoid such unexpected failures. The data that can be obtained from induction machines are non-stationary by nature since the loading may vary during their operation. Wavelet packet decomposition seems to better handle non-stationary nature of induction machines, the use of this method in monitoring applications is limited, since the computational complexity is higher than other methods. In this work four-band wavelet packet decomposition of motor vibration data is proposed to reduce the computational complexity without compromising accuracy. The proposed method is very suitable for parallel computational processing by its nature, and as a result it is predicted that the calculation time will be shortened further if field-progammable gate array is used in design.

scholarly journals Short Circuit and Broken Rotor Faults Severity Discrimination in Induction Machines Using Non-invasive Optical Fiber Technology

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 577
Author(s):  
Belema P. Alalibo ◽  
Bing Ji ◽  
Wenping Cao
Keyword(s):  
Fault Detection ◽  
Optical Fiber ◽  
Condition Monitoring ◽  
Short Circuit ◽  
Induction Machines ◽  
Electric Machines ◽  
Data Logging ◽  
Detection Techniques ◽  
Non Invasive ◽  
Fiber Technology

Multiple techniques continue to be simultaneously utilized in the condition monitoring and fault detection of electric machines, as there is still no single technique that provides an all-round solution to fault finding in these machines. Having various machine fault-detection techniques is useful in allowing the ability to combine two or more in a manner that will provide a more comprehensive application-dependent condition-monitoring solution; especially, given the increasing role these machines are expected to play in man’s transition to a more sustainable environment, where many more electric machines will be required. This paper presents a novel non-invasive optical fiber using a stray flux technique for the condition monitoring and fault detection of induction machines. A giant magnetostrictive transducer, made of terfenol-D, was bonded onto a fiber Bragg grating, to form a composite FBG-T sensor, which utilizes the machines’ stray flux to determine the internal condition of the machine. Three machine conditions were investigated: healthy, broken rotor, and short circuit inter-turn fault. A tri-axial auto-data-logging flux meter was used to obtain stray magnetic flux measurements, and the numerical results obtained with LabView were analyzed in MATLAB. The optimal positioning and sensitivity of the FBG-T sensor were found to be transverse and 19.3810 pm/μT, respectively. The experimental results showed that the FBG-T sensor accurately distinguished each of the three machine conditions using a different order of magnitude of Bragg wavelength shifts, with the most severe fault reaching wavelength shifts of hundreds of picometres (pm) compared to the healthy and broken rotor conditions, which were in the low-to-mid-hundred and high-hundred picometre (pm) range, respectively. A fast Fourier transform (FFT) analysis, performed on the measured stray flux, revealed that the spectral content of the stray flux affected the magnetostrictive behavior of the magnetic dipoles of the terfenol-D transducer, which translated into strain on the fiber gratings.

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