Data-driven fault detection and isolation of system with only state measurements and control inputs using neural networks

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With the advancement of neural network technology, many researchers are trying to find a clever way to apply neural network to a fault detection and isolation area for satisfactory and safer operations of the system. Some researchers detect system faults by combining a concrete model of the system with neural network, generating residuals by neural network, or training neural network with specific sensor signals of the system. In this article, we make a fault detection and isolation neural network algorithm that uses only inherent sensor measurements and control inputs of the system. This algorithm does not need a model of the system, residual generations, or additional sensors. We obtain sensor measurements and control inputs in a discrete-time manner, cut signals with a sliding window approach, and label data with one-hot vectors representing a normal or fault classes. We train our neural network model with the labeled training data. We give 2 neural network models: a stacked long short-term memory neural network and a multilayer perceptron. We test our algorithm with the quadrotor fault simulation and the real experiment. Our algorithm gives nice performance on a fault detection and isolation of the quadrotor.
Publisher
ICROS
Issue Date
2021-10-13
Language
English
Citation

21st International Conference on Control, Automation and Systems (ICCAS), pp.108 - 112

ISSN
2093-7121
DOI
10.23919/ICCAS52745.2021.9650037
URI
http://hdl.handle.net/10203/289826
Appears in Collection
EE-Conference Papers(학술회의논문)
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