Noise diagnostic of induction machine

Janda, M.; Vitek, Ondrej; Skalka, M.

doi:10.1109/icelmach.2010.5608036

Cited by 8 publications

(9 citation statements)

References 3 publications

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“…The condition monitoring and fault identification of the motor require more accurate fault and location detection with an efficient non‐contact method. The non‐contact methods make use of acoustic emission measurement [8, 22], thermal imaging [7, 23] and temperature measurement [6] for detecting the electrical faults. The acoustic emission measurement with direct method uses optical fibre and interferometry.…”

Section: Literature Surveymentioning

confidence: 99%

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Low‐cost SPLL‐based electrical faults identification for three‐phase squirrel cage induction motor using handheld Doppler radar signal analysis

Barusu

Sethurajan

Meganathan

2018

IET Power Electronics

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The condition monitoring of an induction motor performs by contact or non-contact methods. The contact methods execute via vibration signal, temperature measurement, current signature analysis and so on, and the non-contact methods accomplish via thermal imaging, temperature measurement and acoustic emission measurement. The contact method requires human expertise to evaluate failures and it is laborious. The authors propose a novel, low-cost and non-contact method using software phase locked loop (SPLL) for electrical fault identification in the three-phase squirrel cage induction motor. The handheld Doppler ultra wideband radar transmitted signal is focused on the induction motor and the reflected signal is analysed with SPLL. The SPLL error signal correlates the faults. From the experimental results, the fault identification of motor in the earlier stage achieves better accuracy of the different motor fault condition. The pattern of error signal provides the information of various faults in squirrel cage induction motor.

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Section: Literature Surveymentioning

confidence: 99%

“…The overvoltage fault increases the current flow and damages the insulation of the motor. The overvoltage increase by 10% greater than rated input voltage leads to overheating of the motor [22] and causes core loss with saturation. The 10% increase in voltage causes core loss about 20–30%.…”

Section: Three‐phase Squirrel Cage Induction Motor and Faultsmentioning

confidence: 99%

Low‐cost SPLL‐based electrical faults identification for three‐phase squirrel cage induction motor using handheld Doppler radar signal analysis

Barusu

Sethurajan

Meganathan

2018

IET Power Electronics

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“…The main sources of the noise in IM could be classified in three parts [17], [18] mechanical, aerodynamic and electromagnetic. The main source of mechanical noise generation is the same as vibration source -bearings [9], [10] and rotor unbalance (eccentricity) [16].…”

Section: Im Rotor Condition Monitoringmentioning

confidence: 99%

“…The main source of mechanical noise generation is the same as vibration source -bearings [9], [10] and rotor unbalance (eccentricity) [16]. The sources of aerodynamic noise in IM are the fans, rotor wings or cooling ducts [17]. The magnetic stress wave acts in radial directions on the stator and rotor active surfaces causing the electromagnetic noise [17].…”

Section: Im Rotor Condition Monitoringmentioning

confidence: 99%

“…The sources of aerodynamic noise in IM are the fans, rotor wings or cooling ducts [17]. The magnetic stress wave acts in radial directions on the stator and rotor active surfaces causing the electromagnetic noise [17]. Several acoustic monitoring methods are presented nowadays: frequency analysis [19]; partial discharge acoustic measurements [15], speckle noise reduction [11].…”

Section: Im Rotor Condition Monitoringmentioning

confidence: 99%

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Basic Algorithm for Induction Motors Rotor Faults Pre-Determination

Petrov

Rassõlkin

Vaimann

et al. 2017

ETR

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Due to importance of squirrel cage induction motor in today’s industry, the fault detection on that type of motors has become a highly developed area of interest for researchers. The electrical machine is designed for stable operations with minimum noise and vibrations under the normal conditions. When the fault emerges, some additional distortions appear. The necessity to detect the fault in an early stage, to prevent further damage of the equipment due to fault propagation, is one of the most important features of any condition monitoring or diagnostic techniques for electrical machines nowadays. In this paper possible induction motors faults classified and basic algorithm for rotor faults pre-determination is presented.

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Analysis of electromagnetic stiffness in submersible motors with inclined rotor eccentricity

Fang

et al. 2014

2014 17th International Conference on Electrical Machines and Systems (ICEMS)

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Noise diagnostic of induction machine

Cited by 8 publications

References 3 publications

Low‐cost SPLL‐based electrical faults identification for three‐phase squirrel cage induction motor using handheld Doppler radar signal analysis

Low‐cost SPLL‐based electrical faults identification for three‐phase squirrel cage induction motor using handheld Doppler radar signal analysis

Basic Algorithm for Induction Motors Rotor Faults Pre-Determination

Analysis of electromagnetic stiffness in submersible motors with inclined rotor eccentricity

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