Alaa Abdulhady Jaber's Design of an Intelligent Embedded System for Condition PDF
By Alaa Abdulhady Jaber
ISBN-10: 3319449311
ISBN-13: 9783319449319
ISBN-10: 331944932X
ISBN-13: 9783319449326
This thesis introduces a effectively designed and commissioned clever healthiness tracking approach, in particular to be used on any business robotic, which can expect the onset of faults within the joints of the geared transmissions. but the constructed embedded instant tracking method leads itself rather well for functions on any strength transmission apparatus during which the masses and speeds usually are not consistent, and entry is particular. As such this offers major scope for destiny development.
Three major achievements are awarded during this thesis. First, the advance of a situation tracking set of rules in accordance with vibration research of an commercial robotic for fault detection and prognosis. The mixed use of a statistical keep an eye on chart with time-domain sign research for detecting a fault through an arm-mounted instant processor process represents the 1st degree of fault detection. moment, the layout and improvement of a worldly embedded microprocessor base station for on-line implementation of the clever tracking set of rules, and 3rd, the implementation of a discrete wavelet remodel, utilizing a synthetic neural community, with statistical function extraction for robotic fault prognosis during which the vibration signs are first decomposed into 8 degrees of wavelet coefficients.
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Additional info for Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot
Example text
Number of teeth in the gears of joint 3 . . . . . . . . . Fault types in rolling bearings (Harris and Kotzalas 2007a) . Robot’s bearings specification . . . . . . . . . . . . Joint 3 gears and bearings frequencies . . . . . . . . . Frequency bands associated with each level of decomposition . . . . . . . . . . . . . . . . . Activation functions types . . . . . . . . . . . . . . The used parameters for designing the neural network .
The configured parameters in the XBee modules . . . . . Statistical features extracted from time-domain signals captured using NI 6009 DAQ and Arduino DUE board . . .. 58 .. 83 .. 88 . . . 89 . 97 . 105 . 108 . 139 . . . . . . . . 140 167 175 175 189 191 195 200 . 222 xxxv Chapter 1 Introduction Abstract This chapter provides the background to the research presented in this thesis and outlines the motivation for and the rationale behind the study. The main purposes of the different maintenance strategies available are described and condition monitoring technique is discussed in detail and its main steps are explained.
However, the main classes of neural networks are the feed-forward neural network, whose signal direction is from the input to output layer without any feedback connection. That means the past signals are not used for processing the new signals, as illustrated in Fig. 5a. The recurrent (Feedback) Neural Network, which has a feedback connection, and utilizes past signals for identifying the new features, Fig. 5b. There has been significant interest in applying artificial neural networks to identify and diagnose faults in machinery.
Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot by Alaa Abdulhady Jaber
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