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-Abstract
This research investigates different techniques of condition monitoring and fault diagnosis of
rotating machines. These techniques are the classical Fast Fourier Transform (FFT), Discrete
Wavelet Transform (DWT) coupled with different topologies of Neural Networks. A method for
extracting features signal that is a combination of the horizontal and the vertical vibration time
series is proposed. A technique for signal pre-processing for calculating the input feature is also
adopted. The cumulants of magnitude of the vibrations provide a useful set of features for the
detection of unbalance and rub faults. Pre-processing of the vibration signal is showed to amplify
relevant spectral features improving the classification success.
Results based on the data collected with a simple test rig that allow the simulation of rub and/or
unbalance fault(s) are presented. For Neural Networks the results show that the performance of
Self-Organizing Map (SOM) gives higher classification rate than the Feed-Forward Neural
Networks (FFNN). A compound Neural Network with wavelet has classified the correct condition
in over 99% of cases.
DOI
10.21608/erjm.2004.82601
Keywords
Fault Detection Condition Monitoring - Vibration Analysis, Neural network
Authors
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Shoubra Faculty of Engineering
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https://erjm.journals.ekb.eg/article_82601.html
Detail API
https://erjm.journals.ekb.eg/service?article_code=82601
Publication Title
ERJ. Engineering Research Journal
Publication Link
https://erjm.journals.ekb.eg/
MainTitle
CONDITION MONITORING AND FAULT DIAGNOSIS OF ROTATING MACHINERY USING WAVELET AND NEURAL NETWORKS APPROACHES