nach oben

Soft Computing

Erschienen in:

01.04.2015 | Methodologies and Application

Anfis model for vibration signals based on aging process in electric motors

verfasst von: Duygu Bayram, Serhat Şeker

Erschienen in: Soft Computing | Ausgabe 4/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this study, the aging process of an electric motor is accomplished by adaptive neuro-fuzzy inference system (ANFIS) using vibration signals. Different ANFIS models are compared for representing the aging process in the best possible way. An artificial aging experiment is performed and vibration data taken from the initial (healthy) and final (faulty) cases are used to identify the aging process. Four different ANFIS models are presented. Moving average (MA) filters are applied to the input and output pairs for different lagging factors to change the smoothness degree of the data and thus the performance of system identification. The success of the models is evaluated on three conditions; the performance of the ANFIS and the linear correlation between expected output (faulty case data) and aging model output, in time and frequency domains.The study also evaluates the influence of preprocessing using MA filtering on the ANFIS performance for vibration data which have stochastic characteristics.

Vorheriger Artikel Random-key cuckoo search for the travelling salesman problem

Nächster Artikel Dimensionality reduction of medical big data using neural-fuzzy classifier

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Alessio E, Carbone A, Castelli G, Frappietro V (2002) Second-order moving average and scaling of stochastic time series. Eur Phys J B 27(2):197–200. doi:10.1140/epjb/e20020150

Ayaz E, Ozturk A, Seker S (2006) Continuous wavelet transform for bearing damage detection in electric motors. In: Proceedings of circuits and systems for signal processing, information and communication technologies, and power sources and systems, vol 1 and 2, pp 1130–1133

Ayaz E, Öztürk A, Seker S, Upadhyaya BR (2009a) Fault detection based on continuous wavelet transform and sensor fusion in electric motors. COMPEL Int J Comput Math Electr Electron Eng 28(2):454–470CrossRefMATH

Ayaz E, Ucar M, Şeker S, Upadhyaya BR (2009b) Neuro-detector based on coherence analysis for stator insulation in electric motors. Electr Pow Compo Sys 37(5):533–546CrossRef

Bayram D, Seker S (2013) Wavelet based neuro-detector for low frequencies of vibration signals in electric motors. Appl Soft Comput 13(5):2683–2691. doi:10.1016/j.asoc.2012.11.019 CrossRef

Bayram D, Seker S, Upadhyaya BR (2014) Monitoring the aging of industrial motors by geometric trending of frequency domain signatures. J Test Eval 42(4):1–9. doi:10.1520/JTE20130086 CrossRef

Bayram D, Ünnü SY, Şeker S (2012) Lyapunov exponent for aging process in induction motor. In: Proceedings of numerical analysis and applied mathematics ICNAAM 2012, international conference of numerical analysis and applied mathematics, vol 1, AIP Publishing, pp 2257–2261

Bianchini C, Immovilli F, Cocconcelli M, Rubini R, Bellini A (2011) Fault detection of linear bearings in brushless AC linear motors by vibration analysis. IEEE T Ind Electron 58(5):1684–1694. doi:10.1109/Tie.2010.2098354 CrossRef

Concari C, Franceschini G, Tassoni C (2008) Differential diagnosis based on multivariable monitoring to assess induction machine rotor conditions. IEEE T Ind Electron 55(12):4156–4166. doi:10.1109/Tie.2008.2003212 CrossRef

Erbay AS (1999) Multisensor fusion for induction motor aging analysis and fault diagnosis. The University of Tennessee, Knoxville

Eren L, Devaney MJ (2004) Bearing damage detection via wavelet packet decomposition of the stator current. IEEE T Instrum Meas 53(2):431–436. doi:10.1109/Tim.2004.823323 CrossRef

Frosini L, Bassi E (2010) Stator current and motor efficiency as indicators for different types of bearing faults in induction motors. IEEE T Ind Electron 57(1):244–251. doi:10.1109/Tie.2009.2026770 CrossRef

Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. IEEE Trans Autom Control 42:1482–1484. doi:10.1109/TAC.1997.633847

Jang JSR (1993a) Anfis-adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. doi:10.1109/21.256541 CrossRef

Jang JSR (1993b) ANFIS: adaptive-network-based fuzzy inference system. Syst Man Cybern IEEE Trans 23(3):665–685. doi:10.1109/21.256541 CrossRef

Jun C, Suarez J, Molnar P, Behal A (2011) Maximum likelihood parameter estimation in a stochastic resonate-and-fire neuronal model. In: Proceedings of computational advances in bio and medical sciences (ICCABS), 2011 IEEE 1st international conference on, 3–5 Feb 2011, pp 57–62. doi:10.1109/ICCABS.2011.5729941

Karatoprak E, Senguler T, Ayaz E, Caglar R, Seker S (2007) Spectral and statistical based modeling for bearing damage in induction motors. In: Proceedings of diagnostics for electric machines, power electronics and drives, 2007, SDEMPED 2007, IEEE international symposium on, 6–8 Sept 2007, pp 320–325. doi:10.1109/DEMPED.2007.4393115

Lingfei Z, Jun C, Molnar P, Behal A (2012) Weighted least-squares approach for identification of a reduced-order adaptive neuronal model. Neural Netw Learn Syst IEEE Trans 23(5):834–840. doi:10.1109/TNNLS.2012.2187539 CrossRef

McCormick AC, Nandi AK (1999) Bispectral and trispectral features for machine condition diagnosis. IEEE P-Vis Image Sign 146(5):229–234. doi:10.1049/ip-vis:19990673 CrossRef

Nandi S, Toliyat HA (1999) Condition monitoring and fault diagnosis of electrical machines-a review. In: Proceedings of industry applications conference, 1999. Thirty-fourth IAS annual meeting. Conference Record of the 1999 IEEE, vol 191, pp 197–204. doi:10.1109/IAS.1999.799956

Roy N, Purkait P, Bhattacharya K (2005) Application of wavelet and Fourier transforms for vibration analysis of motor. In: Proceedings of Indicon 2005, pp 609–613

Ruqiang Y, Gao RX (2006) Hilbert Huang transform-based vibration signal analysis for machine health monitoring. Instrum Meas IEEE Trans 55(6):2320–2329. doi:10.1109/TIM.2006.887042 CrossRef

Sanz FA, Ramirez JM, Correa RE (2010) Hybrid method for the diagnosis of electrical rotary machines by vibration signals. In: Proceedings of North American power symposium (NAPS), 2010, 26–28 Sept 2010, pp 1–6. doi:10.1109/NAPS.2010.5619959

Seker S (2000) Determination of air-gap eccentricity in electric motors using coherence analysis. Power Eng Rev IEEE 20(7):48–50. doi:10.1109/39.850436 CrossRef

Seker S, Ayaz E (2003a) Feature extraction related to bearing damage in electric motors by wavelet analysis. J Frankl Inst 340(2):125–134. doi:10.1016/S0016-0032(03)00015-2 CrossRefMATH

Seker S, Ayaz E (2003b) A reliability model for induction motor ball bearing degradation. Electr Power Compo Syst 31(7):639–652. doi:10.1080/153250000390203656

Seker S, Ayaz E, Upadhyaya B, Erbay A (2000) Analysis of motor current and vibration signals for detecting bearing damage in electric motors. In: Proceedings of maintenance and reliability conference, Knoxville, TN, USA, Prentice-Hall, pp 29.01–29.14

Siddique A, Yadava GS (2005) A review of stator fault, monitoring techniques of induction motors. IEEE Trans Energy Conver 20(1):106–114. doi:10.1109/Tec.2004.837304 CrossRef

Tavner PJ (2008) Review of condition monitoring of rotating electrical machines. IET Electr Power App 2(4):215–247. doi:10.1049/iet-epa:20070280 CrossRef

Trajin B, Regnier J, Faucher J (2010) Comparison between vibration and stator current analysis for the detection of bearing faults in asynchronous drives. IET Electr Power App 4(2):90–100. doi:10.1049/iet-epa.2009.0040 CrossRef

Wang W, Jianu OA (2010) A smart sensing unit for vibration measurement and monitoring. Mechatron IEEE/ASME Trans 15(1):70–78. doi:10.1109/TMECH.2009.2016956 CrossRef

Werynski P, Roger D, Corton R, Brudny JF (2006) Proposition of a new method for in-service monitoring of the aging of stator winding insulation in ac motors. IEEE Trans Energy Conver 21(3):673–681. doi:10.1109/Tec.2006.875465 CrossRef

Yan RQ, Gao RX (2011) Impact of wavelet basis on vibration analysis for rolling bearing defect diagnosis. In: Proceedings of IEEE IMTC, pp 393–396

Yilmaz MS, Ayaz E (2009) Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data. In: Proceedings of EUROCON 2009, EUROCON ’09. IEEE, 18–23 May 2009, pp 1140–1145. doi:10.1109/EURCON.2009.5167779

Yongzhen Z, Lei C, Wang XS, Jie L (2007) A weighted moving average-based approach for cleaning sensor data. In: Proceedings of distributed computing systems, 2007. ICDCS ’07. 27th international conference on, 25–27 June 2007, pp 38–38. doi:10.1109/ICDCS.2007.83

Titel: Anfis model for vibration signals based on aging process in electric motors
verfasst von: Duygu Bayram
Serhat Şeker
Publikationsdatum: 01.04.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1326-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2015

Bacterially inspired evolution of intelligent systems under constantly changing environments

Max–min hand cropping method for robust hand region extraction in the image-based hand gesture recognition

Study on application of a neuro-fuzzy models in air conditioning systems

The statistical inferences of fuzzy regression based on bootstrap techniques

Biologically inspired deoxyribonucleic acid soft computing for inverse kinematics solver of five-DOF robotic manipulators

A decision rule-based soft computing model for supporting financial performance improvement of the banking industry