Open Access
Issue |
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|
|
---|---|---|
Article Number | 02013 | |
Number of page(s) | 8 | |
Section | Smart Manufacturing and Industrial 4.0 | |
DOI | https://doi.org/10.1051/matecconf/201925502013 | |
Published online | 16 January 2019 |
- R. Damle, J. Rigola, C. Perez-Segarra, J. Castro, A. Oliva, Object-oriented simulation of reciprocating compressors: Numerical verification and experimental comparison, Int J Refrig. 34 (2011) 1989–1998. [CrossRef] [Google Scholar]
- Y. Ma, Z.L. He, X.Y. Peng, Z.W. Xing, Experimental investigation of the discharge valve dynamics in a reciprocating compressor for trans-critical CO2 refrigeration cycle, Appl Therm Eng. 32 (2012) 13–21. [CrossRef] [Google Scholar]
- D.C. Li, H.Q. Wu, J.J. Gao, Experimental study on stepless capacity regulation for reciprocating compressor based on novel rotary control valve, Int J Refrig. 36 (2013) 1701–1715. [CrossRef] [Google Scholar]
- S. Foreman, Compressor valves and unloaders for reciprocating compressors-an OEM perspective. Technical Report, Dresser-Rand Technology Report, 2002. [Google Scholar]
- D. Goebel, Reciprocating compressor suction and discharge valve monitoring. Technical Report, PROGNOST Systems GmbH, 2014. [Google Scholar]
- V.T. Tran, F. AlThobiani, A. Ball, An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks, Expert Syst Appl. 41 (2014) 4113–4122. [CrossRef] [Google Scholar]
- K. Pichler, E. Lughofer, M. Pichler, T. Buchegger, E. Klement, M. Huschenbett, Detecting broken reciprocating compressor valves in the pV diagram, in: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Wollongong, Australia, 2013. [Google Scholar]
- F. Wang, L. Song, L. Zhang, H. Li, Fault diagnosis for reciprocating air compressor valve using p-V indicator diagram and SVM, in: Proceedings of the 3 rd International Symposium on Information Science and Engineering (ISISE), Shanghai, China, 2010. [Google Scholar]
- J. Zhao, S. Wang, Analysis for fatigue failure causes on a large-scale reciprocating compressor vibration by torsional vibration, Procedia Eng. 74 (2014) 170–174. [CrossRef] [Google Scholar]
- M. Elhaj, F. Gu, A.D. Ball, A. Albarbar, M. Al-Qattan, A. Naid, Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring, Mech Syst Signal Pr. 22 (2008) 374–389. [CrossRef] [Google Scholar]
- H.X. Cui, L.B. Zhang, R.Y. Kang, X.Y. Lan, Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method, J Loss Prevent Proc. 22 (2009) 864–867. [CrossRef] [Google Scholar]
- A.M. Al-Ghamd, D. Mba, A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size, Mech Syst Signal Pr. 20 (2006) 1537–1571. [Google Scholar]
- A. Widodo, E.Y. Kim, J.-D. Son, B.-S. Yang, A.C. Tan, D.-S. Gu, B.-K. Choi, J. Mathew, Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine, 36 (2009) 7252–7261. [Google Scholar]
- Y. Hassan Ali, Roslan Abd Rahman, R.I.R. Hamzah, Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review, 69 (2014). [Google Scholar]
- Y.H. Ali, S.M. Ali, R.A. Rahman, R.I.R. Hamzah, Acoustic Emission and Artificial Intelligent Methods in Condition Monitoring of Rotating Machine-A Review. [Google Scholar]
- A.M. Abdelrhman, M.S. Leong, S.A.M. Saeed, S.M. Al Obiadi, A review of vibration monitoring as a diagnostic tool for turbine blade faults, in: Proceedings of the Applied Mechanics and Materials, 2012. [Google Scholar]
- Y. Wang, C. Xue, X. Jia, X. Peng, Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion, Mech Syst Signal Pr. 56-57 (2015) 197–212. [CrossRef] [Google Scholar]
- L. Alfayez, D. Mba, G. Dyson, The application of acoustic emission for detecting incipient cavitation and the best efficiency point of a 60 kW centrifugal pump: case study, NDT&E INT. 38 (2005) 354–358. [CrossRef] [Google Scholar]
- H. Sim, R. Ramli, A. Saifizul, M. Abdullah, Empirical investigation of acoustic emission signals for valve failure identification by using statistical method, Measurement. 58 (2014) 165–174. [CrossRef] [Google Scholar]
- S.J. Vahaviolos, Acoustic emission: standards and technology update, Astm International, 1999. [Google Scholar]
- S.M.A. Al-Obaidi, M.S. Leong, R. Hamzah, A.M. Abdelrhman, A review of acoustic emission technique for machinery condition monitoring: defects detection & diagnostic, Appl Mech Mater. 229 (2012) 1476–1480. [CrossRef] [Google Scholar]
- M.O. Christian U. Grosse, Acoustic emission testing, Springer-Verlag, Heidelberg, Berlin, Germany, 2008. [CrossRef] [Google Scholar]
- S.M.A. Al-Obaidi, M.S. Leong, R.R. Hamzah, A.M. Abdelrhman, M. Danaee, Acoustic emission parameters evaluation in machinery condition monitoring by using the concept of multivariate analysis, 11 (2015) 7507–7514. [Google Scholar]
- S.M. Ali, K. Hui, L. Hee, M.S. Leong, Automated valve fault detection based on acoustic emission parameters and support vector machine, 57 (2018) 491–498. [Google Scholar]
- S.M. Ali, K. Hui, L. Hee, M. Salman Leong, M. Al-Obaidi, Y. Ali, A.M. Abdelrhman, A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters, in: Proceedings of the Materials Science and Engineering Conference Series, 2018. [Google Scholar]
- G. Piccinini, The First computational theory of mind and brain: a close look at mcculloch and pitts’s “logical calculus of ideas immanent in nervous activity”, 141 (2004) 175–215. [Google Scholar]
- G.K. Jha, Artificial neural networks and its applications, (2007). [Google Scholar]
- Y.H. Ali, R. Abd Rahman, R.I.R. Hamzah, Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data, 2015 (2015). [Google Scholar]
- H.M. SOHEL RANA, S. K. SARKAR, Validation and performance analysis of the binary logistic regression model, in: Proceedings of the WSEAS International Conference on Environmental, Medicine and Health Sciences, Penang, Malaysia, 2010. [Google Scholar]
- M.L. Mohammed, R. Mbeleck, D. Patel, D. Niyogi, D.C. Sherrington, B. Saha, Greener and efficient epoxidation of 4-vinyl-1-cyclohexene with polystyrene 2-(aminomethyl) pyridine supported Mo (VI) catalyst in batch and continuous reactors, 94 (2015) 194–203. [Google Scholar]
- A.K. Mahamad, S. Saon, T. Hiyama, Predicting remaining useful life of rotating machinery based artificial neural network, Comput Math Appl. 60 (2010) 1078–1087. [CrossRef] [Google Scholar]
- K.G. Sheela, S. Deepa, Review on methods to fix number of hidden neurons in neural networks, 2013 (2013). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.