Fuzzy decoupled-states multi-model identification of gas turbine operating variables through the use of their operating data

Sidali Aissat; Ahmed Hafaifa; Abdelhamid Iratni; Nadji Hadroug; XiaoQi Chen

doi:10.1016/j.isatra.2022.07.005

Fuzzy decoupled-states multi-model identification of gas turbine operating variables through the use of their operating data

ISA Trans. 2023 Feb:133:384-396. doi: 10.1016/j.isatra.2022.07.005. Epub 2022 Jul 12.

Authors

Sidali Aissat¹, Ahmed Hafaifa², Abdelhamid Iratni³, Nadji Hadroug⁴, XiaoQi Chen⁵

Affiliations

¹ Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, 17000 DZ, Algeria. Electronic address: s.aissat@univ-djelfa.dz.
² Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, 17000 DZ, Algeria; Department of Electrical and Electronics Engineering, Nisantasi University, 34398 Sarıyer, İstanbul, Turkey. Electronic address: a.hafaifa@univ-djelfa.dz.
³ Faculty of Science and Technology, University of Bordj Bou Arreridj, 34030 DZ, Algeria. Electronic address: iratni@univbba.dz.
⁴ 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. Electronic address: n.hadroug@univ-djelfa.dz.
⁵ Manufacturing Futures Research Institute, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Australia. Electronic address: xiaoqichen@swin.edu.au.

PMID: 35868912
DOI: 10.1016/j.isatra.2022.07.005

Abstract

Practically the rotating machines degradation, such as gas turbines, is due to the quality of construction and online operation of their dynamic state models, of different physical phenomena affecting these machines which cause their total malfunction. To maintain their stable operation, it is essential to correctly describe these real dynamic behaviors by reliable and robust representations, by models that can be used in monitoring and diagnostics. To achieve the performance objectives in terms of security, reliability, availability, and operating safety, this work proposes the development of a fuzzy multi-model identification approach with states decoupled from the operating variables, uploaded for monitoring a TITAN 130 turbine. This fuzzy multi-model structure with decoupled states is of interest for the monitoring of industrial systems because it adapts to the different changes in dynamic behavior of the system, makes it possible to represent the nonlinear behavior of the real system in a linear multi-model form without loss of information. In this work, through the different implementations and obtained results, this approach clearly shows how the gas turbine dynamics were reproduced when using the proposed fuzzy multi-models, thus allowing better performance when exploiting it for the synthesis of the faults diagnosis strategy for this rotating machine.

Keywords: Data acquisition; Decoupled states multi-model; Fuzzy logic; Local models; Takagi–Sugeno model.