dc.contributor.author | Paulraj, M. P. | |
dc.contributor.author | Sazali, Yaacob | |
dc.contributor.author | Mohd Zubir, Md Zin | |
dc.date.accessioned | 2010-01-21T01:24:17Z | |
dc.date.available | 2010-01-21T01:24:17Z | |
dc.date.issued | 2009-10-11 | |
dc.identifier.citation | p.2B4 1 - 2B4 5 | en_US |
dc.identifier.uri | http://dspace.unimap.edu.my/123456789/7532 | |
dc.description | Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia. | en_US |
dc.description.abstract | The sound of working vehicle provides an important clue for engine faults diagnosis. Endless efforts have been put into the research of fault diagnosis based on sound. It offers concrete economic benefits, which can lead to high system reliability and save maintenance cost. A number of diagnostic
systems for vehicle repair have been developing in recent years. Artificial Neural Network is a very demanding application and popularly implemented in many industries including condition monitoring via fault diagnosis. This paper presents a feature extraction algorithm using total entropy of 5 level decomposition
of wavelet transform. The engine noise signal is decomposed into 5 levels (A5, D5, A4, D4, A3, D3, A2, D2, A1, D1) using
Daubechies “db4” wavelet family. From the decomposed signals, the entropy is applied for each levels and the feature are extracted and used to develop a functional link neural network. | en_US |
dc.description.sponsorship | Technical sponsored by IEEE Malaysia Section | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universiti Malaysia Perlis | en_US |
dc.relation.ispartofseries | Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009) | en_US |
dc.subject | Entropy | en_US |
dc.subject | Wavelet analysis | en_US |
dc.subject | Functional Link Neural Network | en_US |
dc.subject | Engine -- Diagnosis | en_US |
dc.subject | Engine faults | en_US |
dc.subject | Diagnostic system | en_US |
dc.subject | Neural networks (Computer system) | en_US |
dc.title | Motorbike engine faults diagnosing system using entropy and functional link neural network in wavelet domain | en_US |
dc.type | Working Paper | en_US |