Open Access
| 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 | |
- A. M. Abdelrhman, M. S. Leong, S. A. M. Saeed, and S. M. Al Obiadi, “A Review of Vibration Monitoring as a Diagnostic Tool for Turbine Blade Faults,” Appl. Matt. Res, vol. 229, pp. 1459–1463, 2012. [Google Scholar]
- A. M. Abdelrhman, L. M. Hee, M. S. Leong, and S. Al-Obaidi, “Condition Monitoring of Blade in Turbomachinery: A Review,” Adv. Mech. Eng, vol. 2014, p. 10, 2014. [Google Scholar]
- K. H. Hui, L. M. Hee, M. S. Leong, and A. M. Abdelrhman, “Time-Frequency Signal Analysis in Machinery Fault Diagnosis,” in Adv Mat Res, 2014, pp. 41–45. [Google Scholar]
- K. Mathioudakis, A. Papathanasiou, E. Loukis, and K. Papailiou, “Fast response wall pressure measurement as a means of gas turbine blade fault identification,” JPGC - Pwr, vol. 113, pp. 269–275, 1991. [Google Scholar]
- V. Dedoussis, K. Mathioudakis, and K. Papailiou, “Numerical simulation of blade fault signatures from unsteady wall pressure signals,” JPGC - Pwr, vol. 119, pp. 362–369, 1997. [Google Scholar]
- N. Aretakis and K. Mathioudakis, “Wavelet analysis for gas turbine fault diagnostics,” JPGC - Pwr, vol. 119, 1997. [Google Scholar]
- H. Simmons, “A non-intrusive method for detecting HP blade resonance,” ASME Paper No, 1986 [Google Scholar]
- H. Simmons, “A Non-Intrusive Method for Detecting HP Turbine Blade Resonance,” ASME Paper No, vol. 86, 1986. [Google Scholar]
- P. Parge, Trevillion, B., Carle, P, “Machinery Interactive Display and Analysis System Description and Applications,” in Proceedings of the First International Machinery Monitoring and Diagnostic Conference, Las Vegas, Nevada Sept 11-14, 1989, pp. 176–182. [Google Scholar]
- P. Parge, Trevillion, B., Carle, P, “Non-Intrusive Vibration Monitoring for Turbine Blade Reliability,” in Proceedings of Second International Machinery Monitoring and Diagnostic Conference, Los Angeles, California, Oct 22-25, 1990, pp. Pp. 435–446. [Google Scholar]
- M. H. Lim and M. Salman Leong, “Diagnosis for loose blades in gas turbines using wavelet analysis,” JPGC - Pwr, vol. 127, pp. 314–322, 2005. [Google Scholar]
- K. Mathioudakis, E. Loukis, and K. D. Papailiou, “Casing vibration and gas turbine operating conditions,” JPGC - Pwr, vol. 112, pp. 478–485, 1990. [Google Scholar]
- A. M. Abdelrhman, M. S. Leong, L. M. Hee, and K. H. Hui, “Vibration Analysis of Multi Stages Rotor for Blade Faults Diagnosis,” Adv Mat Res, vol. 845, pp. 133–137, 2014. [Google Scholar]
- Ahmed. M. Abdelrhman, M. S. Leong, L. M. Hee, and W. K. Ngui, “Application of Wavelet Analysis in Blade Faults Diagnosis for Multi-Stages Rotor System,” Appl. Matt. Res, 2013. [Google Scholar]
- A. M. Abdelrhman, M. S. Leong, Y. M. Hamdan, and K. H. Hui, “Time Frequency Analysis for Blade Rub Detection in Multi Stage Rotor System,” in Appl. Matt. Res, 2015, pp. 95–99. [Google Scholar]
- A. M. Abdelrhman, M. S. Leong, L. M. Hee, and W. K. Ngui, “A Comparative Study of Reassigned Conventional Wavelet Transform for Machinery Faults Detection,” in Appl. Matt. Res, 2015, pp. 90–94. [Google Scholar]
- W. K. Ngui, M. Salman Leong, L. Hee, and A. M. Abdelrhman, “Detection of Twisted Blade in Multi Stage Rotor System,” in Appl. Matt. Res, 2015, pp. 144–148. [Google Scholar]
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