Data-driven fault detection and isolation of system with only state measurements and control inputs using neural networks
Jae-Hyeon Park, Dong Eui Chang
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.