Complementary-Integrated Approach to Model-Based and Data-Driven Prognostics and Fault Diagnosis for Reusable Rocket Engine Systems

2021 ◽  
Vol 19 (2) ◽  
pp. 160-169
Author(s):  
Miki HIRABAYASHI ◽  
Daiwa SATOH ◽  
Seiji TSUTSUMI ◽  
Kaname KAWATSU ◽  
Masaki SATO ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Rocket Engine ◽  
Integrated Approach ◽  
Data Driven ◽  
Model Based ◽  
Engine Systems

scholarly journals Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1350 ◽  
Author(s):  
Chen ◽  
Wu ◽  
Wu ◽  
Xiong ◽  
Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Early Warning ◽  
Flight Control ◽  
Data Driven ◽  
Diagnostic Model ◽  
Flight Control System ◽  
Model Based ◽  
On Line

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

Robustness Analysis and Improvement of Fault Diagnosis Model for Nuclear Power Plants Based on Random Forest

2021 ◽  
Author(s):  
Jiangkuan Li ◽  
Meng Lin
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Robustness Analysis ◽  
Noisy Data ◽  
Data Driven ◽  
Model Based ◽  
Diagnosis Model

Abstract With the development of artificial intelligence technology, data-driven methods have become the core of fault diagnosis models in nuclear power plants. Despite the advantages of high flexibility and practicability, data-driven methods may be sensitive to the noise in measurement data, which is inevitable in the process of data measurement in nuclear power plants, especially under fault conditions. In this paper, a fault diagnosis model based on Random Forest (RF) is established. Firstly, its diagnostic performance on noiseless data and noisy data set containing 13 operating conditions (one steady state condition and 12 fault conditions) is analyzed, which shows that the model based on RF has poor robustness under noisy data. In order to improve the robustness of the model under noisy data, a method named ‘Train with Noisy Data’ (TWND) is proposed, the results show that TWND method can effectively improve the robustness of the model based on RF under noisy data, and the degree of improvement is related to the noise levels of added noisy data. This paper can provide reference for robustness analysis and robustness improvement of nuclear power plants fault diagnosis models based on other data-driven methods.

Data-Driven Design for static Model-Based fault diagnosis

2015 ◽  
Author(s):  
Zhangming He ◽  
Jiongqi Wang ◽  
Chen Yin ◽  
Haiyin Zhou ◽  
Dayi Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Static Model ◽  
Data Driven ◽  
Model Based

scholarly journals Integrating Model-Based and Data-Driven Simulator for Health Management of a Reusable Rocket Engine

2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Daiwa Satoh ◽  
Noriyasu Omata ◽  
Miki Hirabayashi ◽  
Seiji Tsutsumi ◽  
Kaname Kawatsu ◽  
...  
Keyword(s):  
Health Management ◽  
Rocket Engine ◽  
Data Driven ◽  
Model Based

A survey of the application of basic data-driven and model-based methods in process monitoring and fault diagnosis

2011 ◽  
Vol 44 (1) ◽  
pp. 12380-12388 ◽  
Author(s):  
S.X. Ding ◽  
P. Zhang ◽  
T. Jeinsch ◽  
E.L. Ding ◽  
P. Engel ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Process Monitoring ◽  
Basic Data ◽  
Data Driven ◽  
Model Based
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