Publication Type : Conference Paper
Publisher : International Conference on Recent Trends in Information Technology, ICRTIT 2011
Source : International Conference on Recent Trends in Information Technology, ICRTIT 2011, Chennai, p.1175-1179 (2011)
ISBN : 9781457705885
Keywords : Acoustic signals, Acoustic waves, Automobile engines, Behavior specifications, Conditioning systems, Data collection, Fault detection, Fault detection systems, Fault signal, Hidden Markov models, HMM, Information technology, Motorbike engine, motorbike fault, Neural networks, Performance evaluation, Training and testing, Two machines, Wavelet denoising
Campus : Coimbatore
Year : 2011
Abstract : 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. © 2011 IEEE.
Cite this Research Publication : R. N. Pravin, Raman, K. G., Kumar, P. P., Bharathi, K. S., and Shajeev, M., “Performance evaluation of HMM and neural network in motorbike fault detection system”, in International Conference on Recent Trends in Information Technology, ICRTIT 2011, Chennai, 2011, pp. 1175-1179.