By Topic

Performance evaluation of HMM and neural network in motorbike fault detection system

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Pravin, R.N. ; Dept. of Electron. & Commun. Eng., Amrita Vishwa Vidyapeetham, Coimbatore, India ; Raman, K.G. ; Kumar, P.P. ; Bharathi, K.S.
more authors

At present, there are many fault conditioning systems for electrical/mechanical systems due to flexibility in data collection for training and testing as entire data can be collected from one or two machines. Only constraint is different machines from which data for training and testing are collected should be of same behavior specifications. But in the case of automobiles, data for training has to be collected from many automobile engines as fault signals will vary in its characteristics from one to another. The acoustic signal produced by a motorbike engine is important information of fault diagnosis in any automobile. Previous approaches have been tried out in silent conditions. Here, acoustic signals are collected in noisy conditions and required preprocessing is done using wavelets to remove the effect of noise. In this paper, hidden markov model and neural network are used for back-end classification and their performance is compared.

Published in:

Recent Trends in Information Technology (ICRTIT), 2011 International Conference on

Date of Conference:

3-5 June 2011