Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique

The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analys...
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Autor*in:	Madhusudhana Reddy Barusu [verfasserIn] Umamaheswari Sethurajan [verfasserIn] Meganathan Deivasigamani [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	infrared imaging condition monitoring signal processing squirrel cage motors phase locked loops fault diagnosis wavelet transforms induction motors fast Fourier transforms vibration measurement rotors temperature measurement noninvasive method rotor bar fault diagnosis three-phase squirrel cage advanced signal processing technique squirrel cage induction motors contact methods noncontact method zero sequence current spectrum measurement nonuniform time resampling motor current spectrum analysis stray flux measurement acoustic emission measurement flux signal analysis acoustic temperature current temperature stray-flux measurement rotor bar fault identification

Übergeordnetes Werk:	In: The Journal of Engineering - Wiley, 2013, (2019)
Übergeordnetes Werk:	year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1049/joe.2018.8242

Katalog-ID:	DOAJ052992705

Internformat


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245	1	0	\|a Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique
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520			\|a The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods..
650		4	\|a infrared imaging
650		4	\|a condition monitoring
650		4	\|a signal processing
650		4	\|a squirrel cage motors
650		4	\|a phase locked loops
650		4	\|a fault diagnosis
650		4	\|a wavelet transforms
650		4	\|a induction motors
650		4	\|a fast Fourier transforms
650		4	\|a vibration measurement
650		4	\|a rotors
650		4	\|a temperature measurement
650		4	\|a noninvasive method
650		4	\|a rotor bar fault diagnosis
650		4	\|a three-phase squirrel cage
650		4	\|a advanced signal processing technique
650		4	\|a squirrel cage induction motors
650		4	\|a contact methods
650		4	\|a noncontact method
650		4	\|a zero sequence current spectrum measurement
650		4	\|a nonuniform time resampling
650		4	\|a motor current spectrum analysis
650		4	\|a stray flux measurement
650		4	\|a acoustic emission measurement
650		4	\|a flux signal analysis
650		4	\|a acoustic temperature
650		4	\|a current temperature
650		4	\|a stray-flux measurement
650		4	\|a rotor bar fault identification
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a Umamaheswari Sethurajan \|e verfasserin \|4 aut
700	0		\|a Meganathan Deivasigamani \|e verfasserin \|4 aut
773	0	8	\|i In \|t The Journal of Engineering \|d Wiley, 2013 \|g (2019) \|w (DE-627)75682270X \|w (DE-600)2727074-9 \|x 20513305 \|7 nnns
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allfields	10.1049/joe.2018.8242 doi (DE-627)DOAJ052992705 (DE-599)DOAJ6ea5aa72a5fd49c8b19487659a35b3e1 DE-627 ger DE-627 rakwb eng TA1-2040 Madhusudhana Reddy Barusu verfasserin aut Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods.. infrared imaging condition monitoring signal processing squirrel cage motors phase locked loops fault diagnosis wavelet transforms induction motors fast Fourier transforms vibration measurement rotors temperature measurement noninvasive method rotor bar fault diagnosis three-phase squirrel cage advanced signal processing technique squirrel cage induction motors contact methods noncontact method zero sequence current spectrum measurement nonuniform time resampling motor current spectrum analysis stray flux measurement acoustic emission measurement flux signal analysis acoustic temperature current temperature stray-flux measurement rotor bar fault identification Engineering (General). Civil engineering (General) Umamaheswari Sethurajan verfasserin aut Meganathan Deivasigamani verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.8242 kostenfrei https://doaj.org/article/6ea5aa72a5fd49c8b19487659a35b3e1 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8242 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
spelling	10.1049/joe.2018.8242 doi (DE-627)DOAJ052992705 (DE-599)DOAJ6ea5aa72a5fd49c8b19487659a35b3e1 DE-627 ger DE-627 rakwb eng TA1-2040 Madhusudhana Reddy Barusu verfasserin aut Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods.. infrared imaging condition monitoring signal processing squirrel cage motors phase locked loops fault diagnosis wavelet transforms induction motors fast Fourier transforms vibration measurement rotors temperature measurement noninvasive method rotor bar fault diagnosis three-phase squirrel cage advanced signal processing technique squirrel cage induction motors contact methods noncontact method zero sequence current spectrum measurement nonuniform time resampling motor current spectrum analysis stray flux measurement acoustic emission measurement flux signal analysis acoustic temperature current temperature stray-flux measurement rotor bar fault identification Engineering (General). Civil engineering (General) Umamaheswari Sethurajan verfasserin aut Meganathan Deivasigamani verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.8242 kostenfrei https://doaj.org/article/6ea5aa72a5fd49c8b19487659a35b3e1 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8242 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfields_unstemmed	10.1049/joe.2018.8242 doi (DE-627)DOAJ052992705 (DE-599)DOAJ6ea5aa72a5fd49c8b19487659a35b3e1 DE-627 ger DE-627 rakwb eng TA1-2040 Madhusudhana Reddy Barusu verfasserin aut Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods.. infrared imaging condition monitoring signal processing squirrel cage motors phase locked loops fault diagnosis wavelet transforms induction motors fast Fourier transforms vibration measurement rotors temperature measurement noninvasive method rotor bar fault diagnosis three-phase squirrel cage advanced signal processing technique squirrel cage induction motors contact methods noncontact method zero sequence current spectrum measurement nonuniform time resampling motor current spectrum analysis stray flux measurement acoustic emission measurement flux signal analysis acoustic temperature current temperature stray-flux measurement rotor bar fault identification Engineering (General). Civil engineering (General) Umamaheswari Sethurajan verfasserin aut Meganathan Deivasigamani verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.8242 kostenfrei https://doaj.org/article/6ea5aa72a5fd49c8b19487659a35b3e1 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8242 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsGer	10.1049/joe.2018.8242 doi (DE-627)DOAJ052992705 (DE-599)DOAJ6ea5aa72a5fd49c8b19487659a35b3e1 DE-627 ger DE-627 rakwb eng TA1-2040 Madhusudhana Reddy Barusu verfasserin aut Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods.. infrared imaging condition monitoring signal processing squirrel cage motors phase locked loops fault diagnosis wavelet transforms induction motors fast Fourier transforms vibration measurement rotors temperature measurement noninvasive method rotor bar fault diagnosis three-phase squirrel cage advanced signal processing technique squirrel cage induction motors contact methods noncontact method zero sequence current spectrum measurement nonuniform time resampling motor current spectrum analysis stray flux measurement acoustic emission measurement flux signal analysis acoustic temperature current temperature stray-flux measurement rotor bar fault identification Engineering (General). Civil engineering (General) Umamaheswari Sethurajan verfasserin aut Meganathan Deivasigamani verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.8242 kostenfrei https://doaj.org/article/6ea5aa72a5fd49c8b19487659a35b3e1 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8242 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsSound	10.1049/joe.2018.8242 doi (DE-627)DOAJ052992705 (DE-599)DOAJ6ea5aa72a5fd49c8b19487659a35b3e1 DE-627 ger DE-627 rakwb eng TA1-2040 Madhusudhana Reddy Barusu verfasserin aut Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods.. infrared imaging condition monitoring signal processing squirrel cage motors phase locked loops fault diagnosis wavelet transforms induction motors fast Fourier transforms vibration measurement rotors temperature measurement noninvasive method rotor bar fault diagnosis three-phase squirrel cage advanced signal processing technique squirrel cage induction motors contact methods noncontact method zero sequence current spectrum measurement nonuniform time resampling motor current spectrum analysis stray flux measurement acoustic emission measurement flux signal analysis acoustic temperature current temperature stray-flux measurement rotor bar fault identification Engineering (General). Civil engineering (General) Umamaheswari Sethurajan verfasserin aut Meganathan Deivasigamani verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2018.8242 kostenfrei https://doaj.org/article/6ea5aa72a5fd49c8b19487659a35b3e1 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8242 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
language	English
source	In The Journal of Engineering (2019) year:2019
sourceStr	In The Journal of Engineering (2019) year:2019
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	infrared imaging condition monitoring signal processing squirrel cage motors phase locked loops fault diagnosis wavelet transforms induction motors fast Fourier transforms vibration measurement rotors temperature measurement noninvasive method rotor bar fault diagnosis three-phase squirrel cage advanced signal processing technique squirrel cage induction motors contact methods noncontact method zero sequence current spectrum measurement nonuniform time resampling motor current spectrum analysis stray flux measurement acoustic emission measurement flux signal analysis acoustic temperature current temperature stray-flux measurement rotor bar fault identification Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	The Journal of Engineering
authorswithroles_txt_mv	Madhusudhana Reddy Barusu @@aut@@ Umamaheswari Sethurajan @@aut@@ Meganathan Deivasigamani @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	75682270X
id	DOAJ052992705
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ052992705</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308171900.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1049/joe.2018.8242</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ052992705</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6ea5aa72a5fd49c8b19487659a35b3e1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Madhusudhana Reddy Barusu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. 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callnumber-first	T - Technology
author	Madhusudhana Reddy Barusu
spellingShingle	Madhusudhana Reddy Barusu misc TA1-2040 misc infrared imaging misc condition monitoring misc signal processing misc squirrel cage motors misc phase locked loops misc fault diagnosis misc wavelet transforms misc induction motors misc fast Fourier transforms misc vibration measurement misc rotors misc temperature measurement misc noninvasive method misc rotor bar fault diagnosis misc three-phase squirrel cage misc advanced signal processing technique misc squirrel cage induction motors misc contact methods misc noncontact method misc zero sequence current spectrum measurement misc nonuniform time resampling misc motor current spectrum analysis misc stray flux measurement misc acoustic emission measurement misc flux signal analysis misc acoustic temperature misc current temperature misc stray-flux measurement misc rotor bar fault identification misc Engineering (General). Civil engineering (General) Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique
authorStr	Madhusudhana Reddy Barusu
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topic_title	TA1-2040 Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique infrared imaging condition monitoring signal processing squirrel cage motors phase locked loops fault diagnosis wavelet transforms induction motors fast Fourier transforms vibration measurement rotors temperature measurement noninvasive method rotor bar fault diagnosis three-phase squirrel cage advanced signal processing technique squirrel cage induction motors contact methods noncontact method zero sequence current spectrum measurement nonuniform time resampling motor current spectrum analysis stray flux measurement acoustic emission measurement flux signal analysis acoustic temperature current temperature stray-flux measurement rotor bar fault identification
topic	misc TA1-2040 misc infrared imaging misc condition monitoring misc signal processing misc squirrel cage motors misc phase locked loops misc fault diagnosis misc wavelet transforms misc induction motors misc fast Fourier transforms misc vibration measurement misc rotors misc temperature measurement misc noninvasive method misc rotor bar fault diagnosis misc three-phase squirrel cage misc advanced signal processing technique misc squirrel cage induction motors misc contact methods misc noncontact method misc zero sequence current spectrum measurement misc nonuniform time resampling misc motor current spectrum analysis misc stray flux measurement misc acoustic emission measurement misc flux signal analysis misc acoustic temperature misc current temperature misc stray-flux measurement misc rotor bar fault identification misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc infrared imaging misc condition monitoring misc signal processing misc squirrel cage motors misc phase locked loops misc fault diagnosis misc wavelet transforms misc induction motors misc fast Fourier transforms misc vibration measurement misc rotors misc temperature measurement misc noninvasive method misc rotor bar fault diagnosis misc three-phase squirrel cage misc advanced signal processing technique misc squirrel cage induction motors misc contact methods misc noncontact method misc zero sequence current spectrum measurement misc nonuniform time resampling misc motor current spectrum analysis misc stray flux measurement misc acoustic emission measurement misc flux signal analysis misc acoustic temperature misc current temperature misc stray-flux measurement misc rotor bar fault identification misc Engineering (General). Civil engineering (General)
topic_browse	misc TA1-2040 misc infrared imaging misc condition monitoring misc signal processing misc squirrel cage motors misc phase locked loops misc fault diagnosis misc wavelet transforms misc induction motors misc fast Fourier transforms misc vibration measurement misc rotors misc temperature measurement misc noninvasive method misc rotor bar fault diagnosis misc three-phase squirrel cage misc advanced signal processing technique misc squirrel cage induction motors misc contact methods misc noncontact method misc zero sequence current spectrum measurement misc nonuniform time resampling misc motor current spectrum analysis misc stray flux measurement misc acoustic emission measurement misc flux signal analysis misc acoustic temperature misc current temperature misc stray-flux measurement misc rotor bar fault identification misc Engineering (General). Civil engineering (General)
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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hierarchy_parent_title	The Journal of Engineering
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title	Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique
ctrlnum	(DE-627)DOAJ052992705 (DE-599)DOAJ6ea5aa72a5fd49c8b19487659a35b3e1
title_full	Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique
author_sort	Madhusudhana Reddy Barusu
journal	The Journal of Engineering
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author_browse	Madhusudhana Reddy Barusu Umamaheswari Sethurajan Meganathan Deivasigamani
class	TA1-2040
format_se	Elektronische Aufsätze
author-letter	Madhusudhana Reddy Barusu
doi_str_mv	10.1049/joe.2018.8242
author2-role	verfasserin
title_sort	non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique
callnumber	TA1-2040
title_auth	Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique
abstract	The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods..
abstractGer	The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods..
abstract_unstemmed	The condition monitoring of the rotor bar in the squirrel cage induction motors (SCIMs) is performed with various contact methods and non-contact methods. The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. The experimental results show that the proposed method achieves better accuracy than the existing methods..
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title_short	Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique
url	https://doi.org/10.1049/joe.2018.8242 https://doaj.org/article/6ea5aa72a5fd49c8b19487659a35b3e1 https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8242 https://doaj.org/toc/2051-3305
remote_bool	true
author2	Umamaheswari Sethurajan Meganathan Deivasigamani
author2Str	Umamaheswari Sethurajan Meganathan Deivasigamani
ppnlink	75682270X
callnumber-subject	TA - General and Civil Engineering
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doi_str	10.1049/joe.2018.8242
callnumber-a	TA1-2040
up_date	2024-07-03T15:11:56.583Z
_version_	1803571184617390080
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The various contact methods are zero sequence current spectrum measurement, non-uniform time resampling of current, motor current spectrum analysis, vibration measurement, etc. Non-contact methods are infrared thermography, stray flux measurement, acoustic emission measurement, temperature measurement, etc. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signal analysis. Whereas, non-contact method accomplishes via acoustic, current, temperature and stray flux measurement. The existing methods suffer from the influence of adjoining machines, surrounding environmental changes; require human expertise to mount sensors and analysing the signals. In this paper, a novel low-cost and non-invasive method proposed for rotor bar fault identification in SCIMs with Software Phase Locked Loop (SPLL). The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The captured signal analysed by Fast Fourier Transform (FFT) and Wavelet transforms to identify the fault. The performance of these transforms compared in term of accuracy to identify the rotor bar faults of SCIM. 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