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Autor(en): 
  • Alaa Abdulhady Jaber
  • Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2016  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Algorithms & data structures / Automation / C / Circuits and Systems / Computerhardware / Control Structures and Microprogramming / Control, Robotics, Automation / Electronic circuits
    ISBN:  9783319449319 
    EAN-Code: 
    9783319449319 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Springer Theses  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  5974 gr 
    Seiten:  279 
    Illustration:  XXXV, 279 p. 145 illus., 113 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This thesis introduces a successfully designed and commissioned intelligent health monitoring system, specifically for use on any industrial robot, which is able to predict the onset of faults in the joints of the geared transmissions. However the developed embedded wireless condition monitoring system leads itself very well for applications on any power transmission equipment in which the loads and speeds are not constant, and access is restricted. As such this provides significant scope for future development. Three significant achievements are presented in this thesis. First, the development of a condition monitoring algorithm based on vibration analysis of an industrial robot for fault detection and diagnosis. The combined use of a statistical control chart with time-domain signal analysis for detecting a fault via an arm-mounted wireless processor system represents the first stage of fault detection. Second, the design and development of a sophisticated embeddedmicroprocessor base station for online implementation of the intelligent condition monitoring algorithm, and third, the implementation of a discrete wavelet transform, using an artificial neural network, with statistical feature extraction for robot fault diagnosis in which the vibration signals are first decomposed into eight levels of wavelet coefficients.

      



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