Faults detection based on fuzzy concepts for vibrations monitoring in gas turbine
Alili Bachir 1  
,   Hafaifa Ahmed 2  
,   Iratni Abdelhamid 3  
More details
Hide details
Gas Turbine Joint Research Team, University of Djelfa, Algeria
Applied Automation and Industrial Diagnostics Laboratory, University of Djelfa, Algeria
Faculty of Science and Technology, University of Bordj Bou Arreridj, 34030 DZ, Algeria
Alili Bachir   

Gas Turbine Joint Research Team, University of Djelfa, Algeria
Submission date: 2020-08-04
Final revision date: 2020-10-22
Acceptance date: 2020-10-30
Online publication date: 2020-11-17
Publication date: 2020-11-17
Diagnostyka 2020;21(4):67–77
The use of new technologies in modern industry improves productivity but induces complexity in the industrial system. This complexity makes it vulnerable to faults, which requires significant expense in terms of safety, reliability and availability. Hence, the diagnosis of gas turbines is a main component for making maintenance decisions for this type of machine. In this paper, the faults detection approach based on fuzzy logic is applied for the vibrations monitoring of a gas turbine, in order to monitor their operating state by including the detection and occurrence of vibration faults, thus using determined fault indicators based on the input / output variables of the examined gas turbine. In this work, the investigation results of fuzzy fault detection approach applied on gas turbine vibration are presented, based on the actual data recorded in the different gas turbine operating modes. However, analysis of the defect detection results was performed in order to determine the influence of these vibration defects on the deferent operating modes of the examined machine. This makes it possible to find the causes of failures and then to deduce the actions to follow the operational safety of the examined turbine.
This work is supported by the Directorate General for Scientific Research and Technological Development (DGRSDT) and was carried out at the Applied Automation and Industrial Diagnostics Laboratory and at the Gas Turbine Joint Research Team in the University of Djelfa, Algeria
Adel Alblawi. Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks. Energy Reports. 2020;6:1083-1096.
Ahmed Zohair Djeddi, Ahmed Hafaifa, Abdellah Kouzou and Salam Abudura. Exploration of reliability algorithms using modified Weibull distribution: Application on gas turbine. International Journal of System Assurance Engineering and Management. 2017;8:1885-1894.
Chaoshun Li, Wen Zou, Nan Zhang, Xinjie Lai. An evolving T–S fuzzy model identification approach based on a special membership function and its application on pump-turbine governing system. Engineering Applications of Artificial Intelligence 2018;69:93-103.
Chuanlai Yuan, Yongyi Liao, Lingshuang Kong, Huiqin Xiao, Fault diagnosis method of distribution network based on time sequence hierarchical fuzzy petri nets. Electric Power Systems Research. 2021, 191:106870.
Cristiano Hora Fontes, Hector Budman. A hybrid clustering approach for multivariate time series – A case study applied to failure analysis in a gas turbine. ISA Transactions. 2017; 71(Part 2): 513-529.
De-long Feng, Ming-qing Xiao, Ying-xi Liu, Hai-fang Song, Zhao Yang, Ze-wen Hu. Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks. Frontiers of Information Technology & Electronic Engineering. 2016;17:1287-1304.
Ehsan Mohammadi, Morteza Montazeri-Gh. A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration. Aerospace Science and Technology. 2015;46:82-93.
Ehsan Mohammadi, Morteza Montazeri-Gh. Active fault tolerant control with self-enrichment capability for gas turbine engines. Aerospace Science and Technology. 2016; 56: 70-89.
Gareth L. Forbes, Robert B. Randall. Estimation of turbine blade natural frequencies from casing pressure and vibration measurements. Mechanical Systems and Signal Processing. 2013; 36(2): 549-561.
Habib Chaouki Ben Djoudi, Ahmed Hafaifa, Dalila Djoudi, Mouloud Guemana, Fault tolerant control of wind turbine via identified fuzzy models prototypes. Diagnostyka. 2020;21(3):3-13.
Hang Gi Lee, Ju Hyun Shin, Chang-Ho Choi, Eunhwan Jeong, Sejin Kwon. Partial admission effect on the performance and vibration of a supersonic impulse turbine. Acta Astronautica. 2018; 145: 105-115.
Houman Hanachi, Jie Liu, Christopher Mechefske. Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system. Chinese Journal of Aeronautics. 2018;31(1):1-9.
Imad Eddine Kaid, Ahmed Hafaifa, Mouloud Guemana, Nadji Hadroug, Abdellah Kouzou, Lakhdar Mazouz, Photovoltaic system failure diagnosis based on adaptive neuro fuzzy inference approach: South Algeria solar power plant. Journal of Cleaner Production. 2018;204:169-182.
Jaroslav Václavík, Jan Chvojan. Torsion vibrations monitoring of turbine shafts. Procedia Structural Integrity. 2017;5:1349-1354.
Junkeon Ahn, Yeelyong Noh, Sung Ho Park, Byung Il Choi, Daejun Chang. Fuzzy-based failure mode and effect analysis (FMEA) of a hybrid molten carbonate fuel cell (MCFC) and gas turbine system for marine propulsion. Journal of Power Sources. 2017; 364: 226-233.
Madhavan S, Rajeev Jain, Sujatha C, Sekhar AS. Vibration based damage detection of rotor blades in a gas turbine engine. Engineering Failure Analysis. 2014;46:26-39.
Marcin Adamowicz, Grzegorz Zywica. Advanced gas turbines health monitoring systems. Diagnostyka. 2018;19(2):77-87.
Mohamed Benrahmoune, Ahmed Hafaifa, Mouloud Guemana and XiaoQi Chen. Detection and modeling vibrational behavior of a gas turbine based on dynamic neural networks approach Journal of Mechanical Engineering. 2018; 68(3): 143-166.
Mohammadreza Tahan, Elias Tsoutsanis, Masdi Muhammad, Abdul Karim Z.A. Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy. 2017; 198: 122-144.
Nadji Hadroug, Ahmed Hafaifa, Abdellah Kouzou, Ahmed Chaibet. Dynamic model linearization of two shafts gas turbine via their input/output data around the equilibrium points. Energy. 2017; 120: 488-497.
Nadji Hadroug, Ahmed Hafaifa, Noureddine Batel, Kouzou Abdellah and Ahmed Chaibet. Active fault tolerant control based on a neuro fuzzy inference system applied to a two shafts gas turbine. Journal of Applied Artificial Intelligence. 2018; 32(6): 515-540.
Qingcai Yang, Shuying Li, Yunpeng Cao. A strong tracking filter based multiple model approach for gas turbine fault diagnosis. Journal of Mechanical Science and Technology. 2018; 32: 465-479.
Rodrigo Berrios, Felipe Núñez, Aldo Cipriano. Fault tolerant measurement system based on Takagi–Sugeno fuzzy models for a gas turbine in a combined cycle power plant. Fuzzy Sets and Systems. 2011; 174(11):114-130.
Sierra-Espinosa F.Z., García J.C. Vibration failure in admission pipe of a steam turbine due to flow instability. Engineering Failure Analysis 2013; 27: 30-40.
Tianyang Wang, Qinkai Han, Fulei Chu, Zhipeng Feng. Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review. Mechanical Systems and Signal Processing. 2019;126:662-685.
Toufik Berredjem, Mohamed Benidir, Bearing faults diagnosis using fuzzy expert system relying on an Improved Range Overlaps and Similarity method. Expert Systems with Applications. 2018; 108: 134-142.
Xin Wu, Yibing Liu. Leakage detection for hydraulic IGV system in gas turbine compressor with recursive ridge regression estimation. Journal of Mechanical Science and Technology. 2017; 31: 4551- 4556.
Yongjia Wu, Haifeng Zhang, Lei Zuo. Thermoelectric energy harvesting for the gas turbine sensing and monitoring system. Energy Conversion and Management 2018; 157:215-223.
Yu Zhang, Chris Bingham, Mike Garlick, Michael Gallimore. Applied fault detection and diagnosis for industrial gas turbine systems. International Journal of Automation and Computing. 2017; 14: 463-473.
Yuning Zhang, Xianghao Zheng, Jinwei Li, Xiaoze Du. Experimental study on the vibrational performance and its physical origins of a prototype reversible pump turbine in the pumped hydro energy storage power station. Renewable Energy. 2019; 130: 667-676.