ORIGINAL PAPER
Detection of Vibrations Defects in Gas Transportation Plant Based on Input / Output Data Analysis: Gas Turbine Investigations
,
 
,
 
,
 
 
 
 
More details
Hide details
1
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, 17000 DZ, Algeria, Gas Turbine Joint Research Team, University of Djelfa, 17000 DZ, Algeria
 
2
Gas Turbine Joint Research Team, University of Djelfa, , Algeria
 
3
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, 17000 DZ, Algeria
 
 
Online publication date: 2020-11-26
 
 
Publication date: 2020-12-01
 
 
International Journal of Applied Mechanics and Engineering 2020;25(4):42-58
 
KEYWORDS
ABSTRACT
In oil and gas industrial production and transportation plants, gas turbines are considered to be the major pieces of equipment exposed to several unstable phenomena presenting a serious danger to their proper operation and to their exploitation. The main objective of this work is to improve the competitiveness performance of this type of equipment by analyses and control of the dynamic behaviors and to develop a fault monitoring system for the equipment exposed and subject to certain eventual anomalies related to the main components, namely the shaft and the rotors. This study will allow the detection and localization of vibration phenomena in the studied gas turbine based on the input / output data.
 
REFERENCES (24)
1.
Bendjama H., Boucherit M.S., Bouhouche S. and Mansour M. (2010): Vibration signal analysis using wavelet transform –PCA-NN technique for fault diagnosis in rotating machinery. – The Mediterranean Journal of Measurment and Control, vol.6, No.4, pp.145-154.
 
2.
Khadersab A. and Shivakumar S. (2010): Vibration analysis techniques for rotating machinery and its effect on bearing faults. – Procedia Manufacturing, vol.20, pp.247-252.
 
3.
Madhavan S., Rajeev Jain, Sujatha C. and Sekhar A.S. (2014): Vibration based damage detection of rotor blades in a gas turbine engine. – Engineering Failure Analysis, vol.46, pp.26-39.
 
4.
Paolo Pennacchi and Andrea Vania (2008): Diagnostics of a crack in a load coupling of a gas turbine using the machine model and the analysis of the shaft vibrations. – Mechanical Systems and Signal Processing, vol.22, No.5, pp.1157-1178.
 
5.
Sandeep Kumar, Niranjan Roy and Ranjan Ganguli (2007): Monitoring low cycle fatigue damage in turbine blade using vibration characteristics. – Mechanical Systems and Signal Processing, vol.21, No.1, pp.480-501.
 
6.
Ahmed Hafaifa, Mouloud Guemana and Attia Daoudi (2015): Vibration supervision in gas turbine based on parity space approach to increasing efficiency. – Journal of Vibration and Control, vol.21, pp.1622-1632.
 
7.
Goudarzi M., Vahidi B., Naghizadeh R.A., Hosseinian S.H. (2015): Improved fault location algorithm for radial distribution systems with discrete and continuous wavelet analysis. – International Journal of Electrical Power ---amp--- Energy Systems, vol.67, pp.423-430.
 
8.
Maria Martinez-Luengo, Athanasios Kolios, Lin Wang (2016): Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm. – Renewable and Sustainable Energy Reviews, vol.64, pp.91-105.
 
9.
Mohamed Ben Rahmoune, Ahmed Hafaifa, Kouzou Abdellah and XiaoQi Chen (2017): Monitoring of high-speed shaft of gas turbine using artificial neural networks: Predictive model application. – DIAGNOSTYKA the Journal of Polish Society of Technical Diagnostics (PSTD), vol.18, No.4, pp.3-10.
 
10.
Christophe Bovet and Laurent Zamponi (2016): An approach for predicting the internal behaviour of ball bearings under high moment load. – Mechanism and Machine Theory, vol.101, pp.1-22.
 
11.
Günyaz Ablay (2013): A modeling and control approach to advanced nuclear power plants with gas turbines. – Energy Conversion and Management, vol.76, pp.899-909.
 
12.
Mishra R.K., Johny Thomas, Srinivasan K., Vaishakhi Nandi and Raghavendra R. Bhatt (2017): Failure analysis of an un-cooled turbine blade in an aero gas turbine engine. – Engineering Failure Analysis, vol.79, pp.836-844.
 
13.
ChunLin Zhang, Bing Li, Bin Qiang Chen, Hong Rui Cao, Yan Yang Zi and Zheng Jia He (2015): Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. – Mechanical Systems and Signal Processing, vol.64-65, pp.162-187.
 
14.
Elias Tsoutsanis, Nader Meskin, Mohieddine Benammar and Khashayar Khorasani (2016): A dynamic prognosis scheme for flexible operation of gas turbines. – Applied Energy, vol.164, pp.686-701.
 
15.
Mohamed Benrahmoune, Ahmed Hafaifa, Mouloud Guemana and XiaoQi Chen (2018): Detection and modeling vibrational behavior of a gas turbine based on dynamic neural networks approach. – Journal of Mechanical Engineering - Strojnícky Časopis, vol.68, No.3, pp.143-166.
 
16.
Pak Kin Wong, Zhixin Yang, Chi Man Vong and Jianhua Zhong (2014): Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. – Neurocomputing, vol.128, pp.249-257.
 
17.
Qu S., Fu C.M., Dong C., Tian J.F., Zhang Z.F. (2013): Failure analysis of the 1st stage blades in gas turbine engine. –Engineering Failure Analysis, vol.32, pp.292-303.
 
18.
Wangpeng He, Yanyang Zi, Binqiang Chen, Feng Wu and Zhengjia He (2015): Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform. – Mechanical Systems and Signal Processing, vol.54-55, pp.457-480.
 
19.
Haidong Shao, Hongkai Jiang, Xingqiu Li and Tianchen Liang (2010): Rolling bearing fault detection using continuous deep belief network with locally linear embedding. – Computers in Industry, vol.96, pp.27-39.
 
20.
Lei Wang, Zhiwen Liu, Qiang Miao and Xin Zhang (2018): Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis. – Mechanical Systems and Signal Processing, vol.103, pp.60-75.
 
21.
Mohammadreza Tahan, Elias Tsoutsanis, Masdi Muhammad, Abdul Karim Z.A. (2017): Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. – Applied Energy, vol.198, pp.122-144.
 
22.
Nadji Hadroug, Ahmed Hafaifa, Abdellah Kouzou and Ahmed Chaibet (2020): Diagnostic of gas turbine defects using a hybrid approach based on a neuro-fuzzy system: Monitoring strategy elaboration. –International Journal of Applied Automation and Industrial Diagnostics, vol.1, No.1, pp.14-27.
 
23.
Sara Nasiri, Mohammad Reza Khosravani and Kerstin Weinberg (2017): Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review. – Engineering Failure Analysis, vol.81, pp.270-293.
 
24.
Shun Li and Jin Wen (2014): A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. – Energy and Buildings, vol.68, pp.63-71.
 
eISSN:2353-9003
ISSN:1734-4492
Journals System - logo
Scroll to top