Fault Diagnosis Method Study for Complex System Based on Kalman Filters with Application to Aircraft Engine

2017 ◽  
Author(s):  
Wei Niu ◽  
Wensheng Niu ◽  
Juan Cheng
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Kalman Filters ◽  
Aircraft Engine ◽  
Diagnosis Method

Fault Diagnosis Method in Complex System Using Bayesian Networks’ Sensitivity Analysis

2014 ◽  
Vol 14 (1) ◽  
pp. 24-30 ◽  
Author(s):  
Runmei Zhang ◽  
Xuegang Hu ◽  
Hao Wang ◽  
Hongliang Yao
Keyword(s):  
Complex System ◽  
Sensitivity Analysis ◽  
Fault Diagnosis ◽  
Bayesian Networks ◽  
Diagnosis Method

Data-Driven Based Gas Path Fault Diagnosis for Turbo-Shaft Engine

2012 ◽  
Vol 249-250 ◽  
pp. 400-404 ◽  
Author(s):  
Feng Lu ◽  
Tie Bin Zhu ◽  
Yi Qiu Lv
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Rbf Neural Network ◽  
Evidence Theory ◽  
Aircraft Engine ◽  
Data Driven ◽  
Support Vector ◽  
Diagnosis Method ◽  
Gas Path Fault

In order to improve diagnostic accuracy and reduce the rate of misdiagnosis to the aircraft engine gas path faulty, the methods based on data-driven and information fusion are developed and analyzed. BP neural network (NN) and RBF neural network based on data-driven single gas path fault diagnosis method is introduced firstly. Design gas path performance estimators and the fault type classification for turbo-shaft engine. Then the gas path fused diagnostic structure based on D-S evidence theory and least squares support vector machine are developed. Comparisons of the turbo-shaft engine gas path fault diagnosis verify the feasibility and effectiveness of the gas path fault diagnosis based on information fusion.

Fuzzy Logic Based Modeling in the Complex System Fault Diagnosis

2011 ◽  
pp. 108-128
Author(s):  
Miroslav Pokorný ◽  
Pavel Fojtík
Keyword(s):  
Complex System ◽  
Decision Making ◽  
Fault Diagnosis ◽  
Kalman Filters ◽  
Fuzzy Model ◽  
Ratio Test ◽  
Linear Dynamic ◽  
Linear Subsystem ◽  
Residual Generator ◽  
Takagi Sugeno

This chapter deals with the model-based fault diagnosis approaches that exploit the fuzzy modeling approximation abilities to obtain the appropriate model of the monitored system. This technique makes use of the Takagi-Sugeno fuzzy model to describe the non-linear dynamic system by its decomposition onto number of linear submodels, so that it is possible to overcome difficulties in conventional methods for dealing with nonlinearity. A linear residual generator formed by Kalman filters which are designed for the each of the linear subsystem is then proposed to generate diagnostic signals - residuals. Since the task is formulated on a statistical basis, the generalized likelihood ratio test is chosen as a decision-making algorithm. Finally, two practical examples are presented to demonstrate the applicability of the proposed approach.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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