Intelligent Condition Monitoring And Diagnosis Systems
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Intelligent Condition Monitoring and Diagnosis Systems
This work covers intelligent system development. In order to survive in an uncertain environment, it is necessary to bring artificial neural networks, fuzzy logic systems, genetic algorithms and expert systems together to make a condition monitoring and diagnosis system more reliable and cost effective than a traditional one. The focus of intelligent condition monitoring and diagnosis system is on practical applications of intelligent techniques. The text provides practicing engineers and scientists with the information they need to solve the problems in both industry and academia.
Condition Monitoring and Control for Intelligent Manufacturing
Author: Lihui Wang
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-05-08
Condition modelling and control is a technique used to enable decision-making in manufacturing processes of interest to researchers and practising engineering. Condition Monitoring and Control for Intelligent Manufacturing will be bought by researchers and graduate students in manufacturing and control and engineering, as well as practising engineers in industries such as automotive and packaging manufacturing.
Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot
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.