Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot

  • Alaa Abdulhady┬áJaber

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xxxv
  2. Alaa Abdulhady Jaber
    Pages 1-10
  3. Alaa Abdulhady Jaber
    Pages 11-51
  4. Alaa Abdulhady Jaber
    Pages 75-91
  5. Alaa Abdulhady Jaber
    Pages 125-155
  6. Alaa Abdulhady Jaber
    Pages 157-180
  7. Alaa Abdulhady Jaber
    Pages 181-207
  8. Alaa Abdulhady Jaber
    Pages 209-230
  9. Alaa Abdulhady Jaber
    Pages 231-241
  10. Back Matter
    Pages 243-279

About this book

Introduction

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 embedded microprocessor 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.

Keywords

Condition Monitoring Industrial Robot Fault Detection and Diagnosis Experimental Modal Analysis Embedded System Wireless Vibration Signal Analysis Wavelet Transform Statistical Control Chart Artificial Neural Network

Authors and affiliations

  • Alaa Abdulhady┬áJaber
    • 1
  1. 1.Dep Mechanical Systems EngineeringNewcastle University Dep Mechanical Systems EngineeringNewcastleUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-44932-6
  • Copyright Information Springer International Publishing Switzerland 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-44931-9
  • Online ISBN 978-3-319-44932-6
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • About this book