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An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques

International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia.

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Bibliographic Details
Main Author: Satyanarayana, M. R. S., Dr.
Other Authors: rssmunukurthi@yahoo.com
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2012
Subjects:
Intelligent Condition Monitoring System (ICMS)
Artificial Neural Networks
Genetic algorithm (GA)
Fault diagnosis
Online Web-based System
Vibration analysis
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/20321
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spelling my.unimap-203212016-06-12T14:29:02Z An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques Satyanarayana, M. R. S., Dr. rssmunukurthi@yahoo.com Intelligent Condition Monitoring System (ICMS) Artificial Neural Networks Genetic algorithm (GA) Fault diagnosis Online Web-based System Vibration analysis International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia. Fault Detection in rotating machinery can be done by regular condition monitoring and vibration analysis of machinery. The task of condition monitoring and fault diagnosis of rotating machinery faults is both significant and important but is often cumbersome and labour intensive. Automating the procedure of feature extraction, fault detection and identification has the advantage of reducing the reliance on experienced personnel with expert knowledge. Various diagnostics methods have been proposed for different types of rotating machinery. However, little research has been conducted on synthesizing and analyzing these techniques, resulting in apprehension when technicians need to choose a technique suitable for application. Various automatic detection methods were introduced, even though they are confined to one or few problems and are not accurate and reliable. Hence automatic fault detection technique are required which are reliable, fast and accurate that can be applied to find solutions to numerous problems. Intelligent condition Monitoring System for fault detection based on expert system is an automated system that detects errors in machinery based on trained neural network model. The objective of the current investigation is to introduce a novel approach to Intelligent Condition Monitoring System which can work with the implementation of Back propagation Neural Network and Radial Basis network and genetic algorithm (which can used for optimization in selecting the network). Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. An ANN was optimized for efficient fault diagnosis in machinery equipment. Also this system is an Online Web-based conditioning monitoring system to be applied successfully in preventing machinery failures and exclusively tested on Air blower. The results are compared with manual calculations and found to be accurate and reliable. 2012-07-14T04:30:54Z 2012-07-14T04:30:54Z 2012-02-27 Working Paper http://hdl.handle.net/123456789/20321 en Proceedings of the International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) Universiti Malaysia Perlis (UniMAP) Pusat Pengajian Kejuruteraan Mekatronik
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Intelligent Condition Monitoring System (ICMS)
Artificial Neural Networks
Genetic algorithm (GA)
Fault diagnosis
Online Web-based System
Vibration analysis
spellingShingle Intelligent Condition Monitoring System (ICMS)
Artificial Neural Networks
Genetic algorithm (GA)
Fault diagnosis
Online Web-based System
Vibration analysis
Satyanarayana, M. R. S., Dr.
An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques
description International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia.
author2 rssmunukurthi@yahoo.com
author_facet rssmunukurthi@yahoo.com
Satyanarayana, M. R. S., Dr.
format Working Paper
author Satyanarayana, M. R. S., Dr.
author_sort Satyanarayana, M. R. S., Dr.
title An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques
title_short An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques
title_full An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques
title_fullStr An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques
title_full_unstemmed An intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques
title_sort intelligent condition monitoring system for fault diagnosis of rotating machinery using expert systems with optimization techniques
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/20321
_version_ 1643793038158331904
score 13.209306

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