Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot (Hardcover, 1st ed. 2017)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Theses
Release date:	September 2016
First published:	2017
Authors:	Alaa Abdulhady Jaber
Dimensions:	235 x 155 x 19mm (L x W x T)
Format:	Hardcover
Pages:	279
Edition:	1st ed. 2017
ISBN-13:	978-3-319-44931-9
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Circuits & components
LSN:	3-319-44931-1
Barcode:	9783319449319

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