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.

实际上，旋转机器（例如燃气轮机）的退化是由于其动态模型的构造质量和在线运行造成的，影响这些机器的不同物理现象导致其完全故障。为了保持它们的稳定运行，必须通过可用于监控和诊断的模型，通过可靠和稳健的表示来正确描述这些真实的动态行为。为了在安全性、可靠性、可用性和操作安全性方面实现性能目标，这项工作提出了一种模糊多模型识别方法的开发，该方法将状态与操作变量解耦，上传用于监控 TITAN 130 涡轮机。这种具有解耦状态的模糊多模型结构对于工业系统的监控很有意义，因为它适应系统动态行为的不同变化，使得在线性多模型中表示真实系统的非线性行为成为可能形式而不会丢失信息。在这项工作中，通过不同的实施和获得的结果，这种方法清楚地展示了在使用所提出的模糊多模型时如何再现燃气轮机动力学，从而在利用它来综合这种旋转的故障诊断策略时允许更好的性能机器。