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 …

Operational modes of a process are described by a number of relevant features that are
indicative of the state of the process. Hundreds of sensors continuously collect data in …

The gas path fault diagnosis is considered widely to ensure the economy, safety and
practicability of gas turbines. Traditional gas path diagnosis methods are vulnerable to …

This paper deals with the basic concepts of sliding mode control of electric drives. Further,
the features of sliding mode control are discussed. The discussions mainly on types of …

Auto-associative neural network (AANN) is a typical nonlinear principal component analysis
method, which is widely used in industry for fault diagnosis purposes, especially in nonlinear …

In this paper, partial kernel principal component analysis (PKPCA) is studied for sensor fault
detection and isolation of an aeroderivative industrial gas turbine. Principal component …

This paper considers the problem of inhibiting smearing effects for multiple sensor fault
isolation. Although the reconstruction-based approach has received considerable attention …

This study addresses the problem of reliable power generation in wind turbines subject to
actuator and sensor faults in the presence of disturbances and uncertainties. For this …

A fault diagnosis solution is proposed in this article for the uncertainty and external
disturbance problems in the main drive system of a rolling mill based on analytical …

For a sensor fault diagnostic system of aircraft engines, the health performance degradation
is an inevitable interference that cannot be neglected. To address this issue, this paper …