A Robust fault diagnosis and forecasting approach based on Kalman filter and Interval Type-2 Fuzzy Logic for efficiency improvement of centrifugal gas compressor system
Bachir Nail 1  
,  
Abdellah Kouzou 1  
,  
Ahmed Hafaifa 1  
,  
Nadji Hadroug 1  
,  
Puig Vicenç 2  
 
 
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1
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa 17000 DZ, Algeria.
2
Automatic Control Department, Universitat Politècnica de Catalunya (UPC), TR11, Rambla de Sant Nebridi, 10, 08222 Terrassa, Spain.
CORRESPONDING AUTHOR
Bachir Nail   

Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa 17000 DZ, Algeria.
Online publish date: 2019-05-11
Publish date: 2019-05-11
Submission date: 2018-10-23
Final revision date: 2019-03-04
Acceptance date: 2019-04-23
 
Diagnostyka 2019;20(2):57–75
KEYWORDS
TOPICS
ABSTRACT
The paper proposes a robust faults detection and forecasting approach for a centrifugal gas compressor system, the mechanism of this approach used the Kalman filter to estimate and filtering the unmeasured states of the studied system based on signals data of the inputs and the outputs that have been collected experimentally on site. The intelligent faults detection expert system is designed based on the interval type-2 fuzzy logic. The present work is achieved by an important task which is the prediction of the remaining time of the system under study to reach the danger and/or the failure stage based on the Auto-regressive Integrated Moving Average (ARIMA) model, where the objective within the industrial application is to set the maintenance schedules in precisely time. The obtained results prove the performance of the proposed faults diagnosis and detection approach which can be used in several heavy industrial systems
 
REFERENCES (40)
1.
Dai X, Gao Z, Breikin T, Wang H. Disturbance attenuation in fault detection of gas turbine engines: A discrete robust observer design, IEEE Transactions on Systems Man and Cybernetics Part C. Applications and Reviews, 2009;39(2):0–239. http://dx.doi.org/10.1109/TSMC....
 
2.
Hafaifa A, Guemana M, Daoudi A. Vibrations supervision in gas turbine based on parity space approach to increasing efficiency. Journal of Vibration and Control, 2015;21(8):1622–1632. https://doi.org/10.1177/107754....
 
3.
Bahareh P, Nader M, Khashayar K. Sensor fault detection, isolation, and identification using multiple-model-based hybrid kalman filter for gas turbine engines. IEEE Transactions on Control Systems Technology, 2015; 24(4): 1184–1200. http://dx.doi.org/10.1109/TCST....
 
4.
Hadroug N, Hafaifa A, Kouzou A, Chaibet A. Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems. Diagnostyka, 2016; 17(4): 3–17.
 
5.
Bassily H, Lund R, Wagner J. Fault detection in multivariate signals with applications to gas turbines. IEEE Transactions on Signal Processing, 2009; 57(3): 835–842.
 
6.
Isermann R. Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer-Verlag Berlin Heidelberg, 2006.
 
7.
Bailey MB, Kreider JF. Creating an automated chiller fault detection and diagnostics tool using a data fault library. ISA Transactions, 2003; 42:485–495.
 
8.
Du Z, Domanski PA, Vance PW. Effect of common faults on the performance of different types of vapor compression systems. Applied Thermal Engineering, 2016;98:61–72. https://doi.org/10.1016/j.appl....
 
9.
Yang Z, Shengwei W, Fu X. A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression. Applied Thermal Engineering, 2013; 51:560–572. http://dx.doi.org/10.1016/j.ap....
 
10.
Tsoutsanis E, Meskin N, Benammar M, Khorasani K. A component map tuning method for performance prediction and diagnostics of gas turbine compressors. Applied Energy, 2014;135(24):572–585. http://dx.doi.org/10.1016/j.ap....
 
11.
Joly R, Ogaji S, Singh R, Probert S. Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine. Applied Energy, 2004;78(4):397–418.
 
12.
Shu Lin, Chunjie Yang, Ping Wu, Zhihuan Song. Active surge control for variable speed axial compressors, ISA Transactions 2014;53:1389–1395. https://doi.org/10.1016/j.isat....
 
13.
Tayarani-Bathaie SS, Khorasani K. Fault detection and isolation of gas turbine engines using a bank of neural networks. Journal of Process Control. 2015;36(12):22-41. https://doi.org/10.1016/j.jpro....
 
14.
Nozaria HA, Shoorehdelib MA, Simanic S, Banadakia HD. Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques. Neurocomputing, 2012; 91(16):29-47. https://doi.org/10.1016/j.neuc....
 
15.
Hafaifa A, Laroussi K, Laaouad F. Robust fuzzy fault detection and isolation approach applied to surge in centrifugal compressor modeling and control. Fuzzy Information and Engineering, 2010; 2(1):49–73.
 
16.
Hafaifa A, Laaouad F, Laroussi K. Fuzzy approach applied in fault detection and isolation to the compression system control. Studies in Informatics and Control (SIC), 2010;19(1):17–26.
 
17.
Hafaifa A, Laaouad F, Laroussi K. Fuzzy logic approach applied to the surge detection and isolation in centrifugal compressor. Automatic Control and Computer Sciences, 2010; 44(1):53–59.
 
18.
Ogaji S, Marinai L, Sampath S, Singh R, Prober S. Gas-turbine fault diagnostics: a fuzzy-logic approach. Applied Energy, 2005;82(1):81–89.
 
19.
Sahar Rahimi Malekshan, Mahdi Aliyari Shoorehdeli, Mostafa Yari. Industrial gas turbine compressor fouling detection based on system identification methods, neural networks and experimental data, 2017:709–714. https://doi.org/10.1109/Irania....
 
20.
Kenyon AD, Catterson VM, McArthur SDJ, Twiddle J. An agent-based implementation of hidden markov models for gas turbine condition monitoring. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014; 44(2):186–195.
 
21.
Sayyid Mahdi Alavinia. Surge avoidance in gas compressor via fault diagnosis. IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2015:1–9. https://doi.org/10.1109/ICECCT....
 
22.
Saavedra I, Bruno JC, Coronas A, Thermodynamic optimization of organic rankine cycles at several condensing temperatures: case study of waste heat recovery in a natural gas compressor station. Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy, 2010; 224(7):917–930.
 
23.
Maleki S, Bingham C, Zhang Y. Development and realization of changepoint analysis for the detection of emerging faults on industrial systems. IEEE Transactions on Industrial Informatics, 2016;12(3):1180–1187.
 
24.
Tabkhi F, Pibouleau L, Hernandez-Rodriguez G, Azzaro-Pantel C, Domenech S. Improving the performance of natural gas pipeline networks fuel consumption minimization problems. AIChE Journal, 2010; 56(4): 946–964.
 
25.
Mohamadi B, Mohamad, Tabkhi F, Sargolzaei J. Exergetic approach to investigate the arrangement of compressors of a pipeline boosting station. Energy Technology, 2014;2(8):732–741.
 
26.
Hafaifa A, Laaouad F, Laroussi K. Fuzzy modelling and control for detection and isolation of surge in industrial centrifugal compressors. Automatic Control Journal of the University of Belgrade, 2009; 19(1): 19–26.
 
27.
Nail B, Kouzou A, Hafaifa A, Chaibet A. Parametric identification and stabilization of turbo-compressor plant based on matrix fraction description using experimental data. Journal of Engineering Science and Technology, 2018; 13(6):1850–1868.
 
28.
Hadroug N, Hafaifa A, Kouzou A, Chaibet A. Dynamic model linearization of two shafts gas turbine via their input/output data around the equilibrium points. Energy, 2017; 120(2):488–497. https://doi.org/10.1016/j.ener....
 
29.
Jiang W, Khan J, Dougal R A. Dynamic centrifugal compressor model for system simulation. Journal of Power Sources, 2006; 158(2):1333–1343.
 
30.
Barszcz T, Zimroz R, Urbanek J, Jabłoński A, Bartelmus W. Bearings fault detection in gas compressor in presence of high level of non-gaussian impulsive noise. Key Engineering Materials, 2013; 569-570(2):473–480. https://doi.org/10.4028/www.sc....
 
31.
Alavinia SM, Khosrowjerdi MJ, Sadrnia MA, Kheiri H, Fateh MM. An algebraic approach to fault detection for surge avoidance in turbo compressor. Journal of Engineering for Gas Turbines and Power, 2014; 137(2):1–8. https://doi.org/10.1115/1.4028....
 
32.
Mohtar H, Chesse P, Chalet D. Describing uncertainties encountered during laboratory turbocharger compressor tests. Experimental Techniques. 2012; 36(5):1–9.
 
33.
Bachir N, Abdellah K, Ahmed H. Robust block roots assignment in linear discrete-time sliding mode control for a class of multivariable system: gas turbine power plant application. Transactions of the Institute of Measurement and Control, 2018:1–17. https://doi.org/10.1177/014233....
 
34.
Akroum M, Hariche K. An optimal instrumental variable identification method for lmfd models. Studies in informatics and control, 2008;17(4): 361–372.
 
35.
Ozek MB, Akpolat ZH. A software tool: Type-2 fuzzy logic toolbox. Computer Applications in Engineering Education, 2008; 16(2):137–146.
 
36.
Manceur M, Essounbouli N, Hamzaoui A. Second-order sliding fuzzy interval type-2 control for an uncertain system with real application. IEEE Transactions on Fuzzy Systems, 2012;20(2):262–275.
 
37.
Box GEP, Jenkins GM, Reinsel GC, Ljung GM. Time series analysis: forecasting and control, 5th edition. Wiley Series in Probability and Statistics, Wiley, 2015.
 
38.
Ting Z, Li L, Xinli Z, Yingkang S, Wenwu S. Time-series approaches for forecasting the number of hospital daily discharged inpatients. IEEE Journal of Biomedical and Health Informatics, 2015; 21(2):515–526. https://doi.org/10.1109/JBHI.2....
 
39.
Taskin A, Kumbasar T. IEEE symposium series on computational intelligence (SSCI) - cape town, South Africa. IEEE symposium series on computational intelligence - an open source matlab/simulink toolbox for interval type-2 fuzzy logic systems, 2015: 1561–1568.
 
40.
Ljung L. System Identification: Theory for the User, 2nd Edition, Prentice Hall, 1999.
 
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