Searched for: subject%3A%22Fault%255C+Identification%22
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document
Ramesh, Nirupama Sai (author)
Model-based fault diagnosis methodologies rely on an accurate mathematical representation of a system's dynamics to effectively detect and localize faults. However, creating such models can be challenging, particularly for complex systems operating under diverse conditions. Furthermore, faults affecting the system can also modify its dynamics....
master thesis 2023
document
Kougiatsos, N. (author), Reppa, V. (author)
This paper proposes a virtual sensor scheme designed to compensate for sensor fault effects in marine fuel engines. The proposed scheme design follows a distributed approach, where the marine fuel engine is decomposed in several subsystems. Then, for each subsystem we design a monitoring agent that can actively compensate for the effects of...
journal article 2022
document
Sapountzoglou, Nikolaos (author), Lago, Jesus (author), Raison, Bertrand (author)
In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid...
journal article 2020
document
SHARMA, Sparsh (author)
The increasing complexity of mechanical systems has resulted in an increased usage and dependence on data driven modelling techniques in order to obtain simple yet accurate models of these systems. Neural networks have emerged as a popular modelling choice due to their proven ability to learn complex nonlinear relationships between inputs and...
master thesis 2019
Searched for: subject%3A%22Fault%255C+Identification%22
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