| Issue |
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|
|
|---|---|---|
| Article Number | 02011 | |
| Number of page(s) | 4 | |
| Section | Smart Manufacturing and Industrial 4.0 | |
| DOI | https://doi.org/10.1051/matecconf/201925502011 | |
| Published online | 16 January 2019 | |
Adapted Wavelet Transform for Twisted Blade Diagnosis in Multi Stage Rotor
1 Department of Mechanical Engineering, School of Engineering, Bahrain Polytechnic, 33349 Isa Town, Kingdom of Bahrain
2 Institute of Noise and Vibration, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
3 Department of Refrigeration and Air-conditioning, Technical College of Mosul, Northern Technical University, Mosul, Iraq
* Corresponding author: ahmedrabak@gmail.com
This paper studies the diagnosis of twisted blade in a multi stages rotor system using adapted wavelet transform and casing vibration. The common detection method (FFT) is effective only if sever blade faults occurred while the minor faults usually remain undetected. Wavelet analysis as alternative technique is still unable to fulfill the fault detection and diagnosis accurately due to its inadequate time-frequency resolution. In this paper, wavelet is adapted and its time-frequency is improved. Experimental study was undertaken to simulate multi stages rotor system. Results showed that the adapted wavelet analysis is effective in twisted blade diagnosis compared to the conventional one.
© The Authors, published by EDP Sciences, 2019
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