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

Abstract

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.