scholarly journals Multi-Fault Diagnosis of an Aero-Engine Control System Using Joint Sliding Mode Observers

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 10186-10197 ◽  
Author(s):  
Linfeng Gou ◽  
Yawen Shen ◽  
Hua Zheng ◽  
Xianyi Zeng
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Sliding Mode ◽  
Engine Control ◽  
Engine Control System ◽  
Aero Engine ◽  
Sliding Mode Observers

scholarly journals FDIA System for Sensors of the Aero-Engine Control System Based on the Immune Fusion Kalman Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Linfeng Gou ◽  
Ruiqian Sun ◽  
Xiaobao Han
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Fault Diagnosis ◽  
Filter Bank ◽  
Estimation Accuracy ◽  
Engine Control ◽  
Engine Control System ◽  
Multiple Sensors ◽  
Aero Engine ◽  
Non Gaussian

The Kalman filter plays an important role in the field of aero-engine control system fault diagnosis. However, the design of the Kalman filter bank is complex, the structure is fixed, and the parameter estimation accuracy in the non-Gaussian environment is low. In this study, a new filtering method, immune fusion Kalman filter, was proposed based on the artificial immune system (AIS) theory and the Kalman filter algorithm. The proposed method was used to establish the fault diagnosis, isolation, and accommodation (FDIA) system for sensors of the aero-engine control system. Through a filtering calculation, the FDIA system reconstructs the measured parameters of the faulty sensor to ensure the reliable operation of the aero engine. The AIS antibody library based on single sensor fault was constructed, and with feature combination and library update, the FDIA system can reconstruct the measured values of multiple sensors. The evaluation of the FDIA system performance is based on the Monte Carlo method. Both steady and transient simulation experiments show that, under the non-Gaussian environment, the diagnosis and isolation accuracy of the immune fusion Kalman filter is above 95%, much higher than that of the Kalman filter bank, and compared with the Kalman particle filter, the reconstruction value is smoother, more accurate, and less affected by noise.

On-line performance optimisation of aero engine control system

Automatica ◽  
2003 ◽  
Vol 39 (12) ◽  
pp. 2115-2121 ◽  
Author(s):  
O.D. Lyantsev ◽  
T.V. Breikin ◽  
G.G. Kulikov ◽  
V.Y. Arkov
Keyword(s):  
Control System ◽  
Engine Control ◽  
Engine Control System ◽  
Aero Engine ◽  
On Line

scholarly journals Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine

2019 ◽  
Vol 9 (19) ◽  
pp. 4122 ◽  
Author(s):  
Bo Wang ◽  
Hongwei Ke ◽  
Xiaodong Ma ◽  
Bing Yu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Engine Control ◽  
Piston Engine ◽  
Engine Control System ◽  
Diagnosis Method

Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.

Discussion About Dynamic Simulation Test of an Aero-Engine Control System

1985 ◽  
Author(s):  
Wu Chi Hua ◽  
Fan Ding
Keyword(s):  
Control System ◽  
Control Systems ◽  
Dynamic Simulation ◽  
Physical Simulation ◽  
Engine Control ◽  
Simulation Test ◽  
Engine Control System ◽  
Aero Engine

In this paper, following viewes are expressed: (1). Introduction of partly physical simulation test about aero-engine control systems. (2). Several plans carried out for this test. (3). A real example of digital analogue hybrid partly physical simulation test.

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