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Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection.
Tian, An-Hong; Fu, Cheng-Biao; Li, Yu-Chung; Yau, Her-Terng.
Afiliación
  • Tian AH; College of Information Engineering, Qujing Normal University, Qujing 655011, China. tianah@mail.qjnu.edu.cn.
  • Fu CB; College of Information Engineering, Qujing Normal University, Qujing 655011, China. fucb@mail.qjnu.edu.cn.
  • Li YC; Department of Mechanical Engineering, National Cheng Kung University, 1 University Road, Tainan City 701, Taiwan. a238966777@gmail.com.
  • Yau HT; Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan. pan1012@ms52.hinet.net.
Sensors (Basel) ; 18(9)2018 Sep 12.
Article en En | MEDLINE | ID: mdl-30213131
ABSTRACT
In this study we used a non-autonomous Chua's circuit, and the fractional Lorenz chaos system. This was combined with the Extension theory detection method to analyze the voltage signals. The bearing vibration signals, measured using an acceleration sensor, were introduced into the master and slave systems through a Chua's circuit. In a chaotic system, minor differences can cause significant changes that generate dynamic errors. The matter-element model extension can be used to determine the bearing condition. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from discrete Fourier transform analysis, wavelet analysis and an integer order chaos system. The diagnostic rate of the fractional-order master and slave chaotic system could reach 100% if the fractional-order parameter adjustment was used. This study presents a very efficient and inexpensive method for monitoring the state of ball bearings.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2018 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2018 Tipo del documento: Article País de afiliación: China