Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis

Abstract

Fault diagnosis for aircraft fuel system can not only improve flight security, but also reduce the huge cost due to regular maintenance. It remains a problem because of the complicated system and the heterogeneous failure modes, especially the different failure modes that have similar impacts on the system. This paper uses the deep quantum inspired neural network (DQINN) which is an improved deep quantum network (DQN) to solve such problem. This method is the combination of classical deep belief network (DBN) and quantum inspired neural network (QINN). For the purpose of inheriting the advantages of DBN and QINN, the structure of DQINN is built in a new fashion. From a system perspective, the DQINN is constructed by the linear superposition of multiple DBNs with quantum intervals in the last hidden layer. Experiments conducted on standard datasets show that DQINN outperforms other three classical algorithms. Finally, a normal model of aircraft fuel system is built and four kinds of common failure modes of the core components are injected into this model, respectively. And the DQINN is applied to the aircraft fuel system fault diagnosis.