In this study, an efficient strategy for fault detection and isolation (FDI) of an Industrial Gas
Turbine is introduced based on ensemble learning methods. Four independent Wiener …

Effective fault detection, estimation, and isolation are essential for the safety and reliability of
gas turbines. In this article, a hybrid fault detection and isolation (FDI) approach is presented …

In this paper a novel fault detection, isolation, and identification (FDI&E) scheme using a
coupling diagnosis method with the integration of the model-based method and …

This study proposes a model-based robust fault detection and isolation (RFDI) method with
hybrid structure. Robust detection and isolation of the realistic faults of an industrial gas …

Effective fault diagnosis approach in gas turbines is crucial for ensuring reliable and efficient
operations, minimizing downtime, and mitigating safety risks. In this article, a robust fault …

In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines
is proposed by developing an ensemble of dynamic neural network identifiers. For health …

With the increased availability of condition monitoring data and the increased complexity of
explicit system physics-based models, the application of data-driven approaches for fault …

Renewable energy sources like wind energy are widely available without any limitation.
Reliability of wind turbine is crucial in extracting the maximum amount of energy from the …

In this paper, a Fault Detection and Isolation (FDI) system based on an ensemble-based
hierarchical classifier is devised to detect and isolate twelve typical turbine faulty scenarios …

This paper investigates a new scheme for fault detection and isolation based on time series
and data analysis. This scheme is applied in wind turbines which are used to tap the …