IMA health state evaluation using deep feature learning with quantum neural network

Abstract

Integrated modular avionics is one of the most advanced systems. Its performance deeply impacts on the working condition of aircraft. In order to enhance the safety and reliability of aircraft, the health state of the integrated modular avionics should be evaluated accurately. In this paper, a novel deep learning method is developed to evaluate the health state. Firstly, as one of the deep learning methods, stacked denoising autoencoders is used to extract the features from the raw data immediately to retain original information. Secondly, the extracted features are fed into the quantum neural network to classify the data set. The loss function of the quantum neural network is evolved to improve the classification performance. Experiments conducted on standard datasets show that the proposed method is more effective and robust than other four conventional algorithms. Finally, this paper builds an integrated modular avionics degradation model by the changing probability of the occurrence of soft faults in the whole life serves and the proposed method is applied to the health state evaluation.