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Design of an intelligent embedded sy...

Jaber, Alaa Abdulhady.

Design of an intelligent embedded system for condition monitoring of an industrial robot

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Design of an intelligent embedded system for condition monitoring of an industrial robotby Alaa Abdulhady Jaber.
作者:	Jaber, Alaa Abdulhady.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xxxv, 279 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Robots, Industrial.
電子資源:	http://dx.doi.org/10.1007/978-3-319-44932-6
ISBN:	9783319449326$q(electronic bk.)

Design of an intelligent embedded system for condition monitoring of an industrial robot
Jaber, Alaa Abdulhady.

Design of an intelligent embedded system for condition monitoring of an industrial robot[electronic resource] /by Alaa Abdulhady Jaber. - Cham :Springer International Publishing :2017. - xxxv, 279 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Chapter 1 Introduction -- Chapter 2 Literature Review -- Chapter 3 Signal Processing Techniques for Condition Monitoring -- Chapter 4 Puma 560 Robot and its Dynamic Characteristics -- Chapter 5 Robot Hardware, Transmission Faults and Data Acquisition -- Chapter 6 Robot Vibration Analysis and Feature Extraction -- Chapter 7 Intelligent Condition Monitoring System Design -- Chapter 8 Embedded System Design -- Chapter 9 Embedded Software Design, System Testing and Validation -- Chapter 10 Conclusions and Future Work -- References -- Appendices.

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.

ISBN: 9783319449326$q(electronic bk.)

Standard No.: 10.1007/978-3-319-44932-6doiSubjects--Topical Terms:

267734
Robots, Industrial.

LC Class. No.: TS191.8

Dewey Class. No.: 629.892

Design of an intelligent embedded system for condition monitoring of an industrial robot
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520 $a 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.
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