Enhanced Quantum Neural Network Integrated in Health State Estimation for Aircraft Fuel System

2018 ◽  
Author(s):  
Zehai Gao ◽  
Cunbao Ma ◽  
Xu Dong
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Health State ◽  
Fuel System ◽  
Quantum Neural Network ◽  
Aircraft Fuel

Fault Detection for Aircraft Fuel System with Neural Network

2020 ◽  
Author(s):  
Nithya Subramanian ◽  
Hongmei He ◽  
Ian Jennions
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fuel System ◽  
Aircraft Fuel

scholarly journals Circuit Health State Estimation via An Integrated Deep Neural Network

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Short Term Memory ◽  
Health Management ◽  
Angular Distance ◽  
System Level ◽  
Health State ◽  
Convolutional Autoencoder ◽  
External Conditions ◽  
The Time Domain

accelerating the industry to become more integrated and intricated. It is almost inevitable for a system to encounter failures during its whole life span. Thus, it is imperative to monitor the operating system from a system-level perspective to avoid potential catastrophes. Intuitively, inclusive prior knowledge is required for prognostics and health management (PHM). However, due to time-varying parameters and external conditions, the system is usually too complex to neatly fit into a prior-built model. This paper presents a novel pragmatic method, encompassing the convolutional autoencoder (CAE) and long short-term memory recurrent neural network (LSTM-RNN), to track the health state of a circuit. Briefly, the proposed method can be divided into two steps. First, degradation characteristics are extracted by using the time-domain features and CAE to prepare for the later health state estimation step. Then, the LSTM-RNN is used to finish the predictive process, i.e., to map the extracted abstract features to the health state. In addition, the degradation of a practical circuit considering the angular distance is discussed to quantify the health state of the circuit system. Furthermore, a case study based on that prognostics scheme is conducted to verify the proposed method. The comparison with other existing popular methods indicates the superiority of the proposed methodology.

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

2017 ◽  
Vol 238 ◽  
pp. 13-23 ◽  
Author(s):  
Zehai Gao ◽  
Cunbao Ma ◽  
Dong Song ◽  
Yang Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fuel System ◽  
Aircraft Fuel

scholarly journals Utilization of Low-Potential Energy Through to the Use in Aircraft Fuel System

2021 ◽  
Vol 1102 (1) ◽  
pp. 012007
Author(s):  
Renat R. Badykov ◽  
Roman A. Panshin ◽  
Olga V. Tremkina ◽  
Anastasia A. Prokofieva
Keyword(s):  
Potential Energy ◽  
Fuel System ◽  
Aircraft Fuel

scholarly journals Cross-Modal Health State Estimation

2018 ◽  
Author(s):  
Nitish Nag ◽  
Vaibhav Pandey ◽  
Preston J. Putzel ◽  
Hari Bhimaraju ◽  
Srikanth Krishnan ◽  
...  
Keyword(s):  
State Estimation ◽  
Health State
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