Gas turbine reliability estimation to reduce the risk of failure occurrence with a comparative study between the two-parameter Weibull distribution and a new modified Weibull distribution
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Gas Turbine Joint Research Team, University of Djelfa
Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria
Faculty of Science and Technology, University of Bordj Bou Arreridj, 34030 DZ, Algeria
Submission date: 2021-08-29
Final revision date: 2021-11-03
Acceptance date: 2022-01-29
Online publication date: 2022-02-10
Publication date: 2022-02-10
Corresponding author
Djeddi Ahmed Zohair   

Gas Turbine Joint Research Team, University of Djelfa
Diagnostyka 2022;23(1):2022107
Responding to the needs of quality and robustness of analysis and management of degradation of equipment, to increase their life cycle and to expand these facilities to become more and more sophisticated and agronomic. This work proposes a contribution to increase the survival of a gas turbine, installed in a gas-compression plant, with a comparative study between the two-parameter Weibull distribution. A new modified Weibull distribution was proposed also to reduce the risk of occurrence of failure in this rotating machine. A Statistical analysis and validation on the synthesis of turbine's reliability data and failures were considered, with a particular focus on the use of this data to increase the availability of this type of machine. So, developing a maintenance plan based on their reliability indices for scheduled inspections.
Alblawi A. 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.
Djeddi AZ, Hafaifa A, Salam A. Operational reliability analysis applied to a gas turbine based on three parameter Weibull distribution. Mechanics, 2015; 21(3):187−192.
Djeddi AZ, Hafaifa A, Kouzou A, Abudura S. Exploration of reliability algorithms using modified Weibull distribution: Application on gas turbine. International Journal of System Assurance Engineering and Management, 2017;8:1885-1894.
Djeddi AZ, Hafaifa A, Salam A. Gas turbine reliability model based on tangent hyperbolic reliability function. Journal of Theoretical and Applied Mechanics, 2015; 53(3):723-730.
Jeddi AZ, Hafaifa A, Guemana M, Kouzou A. Gas turbine reliability modelling based on a bath shaped rate failure function: modified Weibull distribution validation. Life Cycle Reliability and Safety Engineering, 2020;9:437-448.
Barak MS, Reena G, Ajay K. Reliability measures analysis of a milk plant using RPGT. Life Cycle Reliability and Safety Engineering, 2021; 10: 295-302.
Mohamed BR, Hafaifa A, Kouzou A, XiaoQi C. Monitoring of high-speed shaft of gas turbine using artificial neural networks: predictive model application. Diagnostyka, 2017; 18(4):3-10.
Saadat B, Kouzou A, Guemana M, Hafaifa A. Availability phase estimation in gas turbine based on prognostic system modeling. Diagnostyka, 2017;18(2):3-11.
Djeddi C, Hafaifa A, Iratni A, Hadroug N, Chen XQ. Robust diagnosis with high protection to gas turbine failures identification based on a fuzzy neuro inference monitoring approach. Journal of Manufacturing Systems, 2021;59:190-213.
Zhou D, Huang D, Hao J, Wu H, Chang C, Zhang H. Fault diagnosis of gas turbines with thermodynamic analysis restraining the interference of boundary conditions based on STN. International Journal of Mechanical Sciences, 2021; 191:106053.
Zhou D, Yao Q, Wu H, Ma S, Zhang H. Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks. Energy, 2020;200: 117467.
Halimi D, Hafaifa A, Bouali E. Maintenance actions planning in industrial centrifugal compressor based on failure analysis. The Quarterly Journal of Maintenance and Reliability, 2014;16(1):17-21.
Han D, Tian J, Xue P, Shi P. A novel intelligent fault diagnosis method based on dual convolutional neural network with multi-level information fusion. Journal of Mechanical Science and Technology, 2021; 35:3331-3345.
Ramos E, Ramos PL, Louzada F. Posterior properties of the Weibull distribution for censored data. Statistics & Probability Letters, 2020; 166:108873.
Herman Shen MH. Reliability assessment of high cycle fatigue design of gas turbine blades using the probabilistic Goodman Diagram. International Journal of Fatigue, 1999;21(7):699-708.
Zhao J, Oh U, Lee Y, Park J, Choi J. A study on reliability and capacity credit evaluation of China power system considering WTG with multi energy storage systems. Journal of Electrical Engineering & Technology, 2021;16:2367-2378.
Błachnio J, Spychała J, Zasada D. Analysis of structural changes in a gas turbine blade as a result of high temperature and stress. Engineering Failure Analysis, 2021;127:105554.
Song KL, Bai GC, Li XQ, Wen J. A unified fatigue reliability-based design optimization framework for aircraft turbine disk. International Journal of Fatigue, 2021;152:106422.
Verma M, Kumar A. A novel general approach to evaluating the reliability of gas turbine system. Engineering Applications of Artificial Intelligence, 2014;28:13-21.
Bai M, Yang X, Liu J, Liu J, Yu D. Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers. Applied Energy, 2021;302: 117509.
Ali MA, Barakat MM, Abokhalaf MM, Fadel YH, Kandil M, Rasmy MW, Ali ON, Besheer AH, Emara HM, Bahgat A. Micro-grid monitoring and supervision: Web-based SCADA approach. Journal of Electrical Engineering & Technology, 2021;16:2313-2331.
Rahmoune MB, Hafaifa A, Kouzou A, Chen XQ, Chaibet A. Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling. Mathematics and Computers in Simulation, 2021;179:23-47.
Gh MM, Yazdani S. Application of interval type-2 fuzzy logic systems to gas turbine fault diagnosis. Applied Soft Computing, 2020;96:106703.
Gh MM, Nekoonam A, Yazdani S. A novel approach to gas turbine fault diagnosis based on learning of fault characteristic maps using hybrid residual compensation extreme learning machine-growing neural gas model. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2021;43(9):1-15.
Guemana M, Hafaifa A Rahmoune MB. Reliability study of gas turbines for improving their availability by ensuring optimal exploitation. OIL GAS European Magazine, 2015;2:88-91.
Hadroug N, Hafaifa A, Iratni A, Guemana M. Reliability modeling using an adaptive neuro-fuzzy inference system: Gas turbine application. Fuzzy Information and Engineering, 2021.
Hadroug N, Hafaifa A, Kouzou A, Chaibet A. Improvement of gas turbine availability using reliability modeling based on fuzzy system. Chapter in Applied Condition Monitoring book series, ICDT 2016: Advances in Technical Diagnostics, 2018;10: 15-28.
Wong PK, Yang Z, Vong CN, Zhong J. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing, 2014; 128:249-257.
Balducci P, Mongird K, Weimar M. Understanding the value of energy storage for power system reliability and resilience applications. Current Sustainable and Renewable Energy Reports, 2021;8: 131-137.
Strzelecki P. Determination of fatigue life for low probability of failure for different stress levels using 3-parameter Weibull distribution. International Journal of Fatigue, 2021;145:106080.
Tryon RG, Cruse TA, Mahadevan S. Development of a reliability-based fatigue life model for gas turbine engine structures. Engineering Fracture Mechanics, 1996; 53(5): 807-828.
Sun R, Shi L, Yang X, -Wang Y, Zhao Q. A coupling diagnosis method of sensors faults in gas turbine control system. Energy, 2020;205:117999.
Amirkhani S, Chaibakhsh A, Ghaffari A. Nonlinear robust fault diagnosis of power plant gas turbine using Monte Carlo-based adaptive threshold approach. ISA Transactions, 2020;100:171-184.
Rahme S, Meskin N. Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine. Control Engineering Practice, 2015;38: 57-74.
Park S, Shin J, Morishita M, Saitoh T, Choi G, Tanahashi M. Validation of measured data on F/A ratio and turbine inlet temperature with optimal estimation to enhance the reliability on a full-scale gas turbine combustion test for IGCC. International Journal of Hydrogen Energy, 2019;44(26):13999-14011.
Yazdani S, Skates MH, Holden G. Adding more by using less: Adaptive reuse of woolstores. Procedia Engineering, 2017;180:697-703.
Yazdani S, Gh MM. A novel gas turbine fault detection and identification strategy based on hybrid dimensionality reduction and uncertain rule-based fuzzy logic. Computers in Industry, 2020;115:103131.
Zhong SS, Fu S, Lin L. A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement, 2019;137:435-453.
Aissat S, Hafaifa A, Iratni A, Guemana M. Identification of two-shaft gas turbine variables using a decoupled multi-model approach with genetic algorithm. Periodica Polytechnica Mechanical Engineering, 2021; 65(3): 229-245.
Simani S, Fantuzzi C. Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype. Mechatronics, 2006;16(6):341-363.
Olsson T, Ramentol E, Rahman M, Oostveen M, Kyprianidis K. A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines. Energy and AI, 2021; 4:100064.
Xiao YQ, Liu ZY, Zhu W, Peng XM. Reliability assessment and lifetime prediction of TBCs on gas turbine blades considering thermal mismatch and interfacial oxidation. Surface and Coatings Technology, 2021;423:127572.
Li XQ, Bai GC, Song LK, Wen J. Fatigue reliability estimation framework for turbine rotor using multi-agent collaborative modeling. Structures, 2021;29: 1967-1978.
Yang X, Bai M, Liu J, Liu J, Yu D. Gas path fault diagnosis for gas turbine group based on deep transfer learning. Measurement, 2021;181:109631.
Shen Y, Khorasani K. Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines. Neural Networks, 2020;130:126-142.
Chen YZ, Zhao XD, Xiang HC, Tsoutsanis E. A sequential model-based approach for gas turbine performance diagnostics. Energy, 2021; 220:119657.
Liao Z, Wang J, Liu J, Geng J, Li M, Chen X, Song Z. Uncertainties in gas-path diagnosis of gas turbines: Representation and impact analysis. Aerospace Science and Technology, 2021;113:106724.
Liu ZH, Meng XD, Wei HL, Chen L, Lu BL, Wang ZH, Chen L. A regularized LSTM method for predicting remaining useful life of rolling bearings. International Journal of Automation and Computing, 2021;18:581-593.
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