Research and Verification of Fault Diagnosis Method for Avionics System Based on Data Mining

2019 ◽  
Author(s):  
Linlin Shi ◽  
Zhenwei Zhou ◽  
Yun Huang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Diagnosis Method

Spacecraft Fault Diagnosis Based on Telemetry Data Mining and Fault Tree Analysis and Design of Expert System

2013 ◽  
Vol 760-762 ◽  
pp. 1062-1066 ◽  
Author(s):  
Xiang Gao ◽  
Tao Zhang ◽  
Hong Jin Liu ◽  
Jian Gong
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Telemetry Data ◽  
Tree Analysis ◽  
Analysis And Design ◽  
Diagnosis Method ◽  
The Impact

In this paper, a fault diagnosis method for spacecraft based on telemetry data mining and fault tree analysis was proposed. Decision trees are constructed from the history telemetry data of the spacecraft, and are used to classify the current data which is unknown whether it is fault. If there is a fault, the fault tree method will be used to analyze the fault reason and the impact on the spacecraft system. This method can effectively solve the problem of diagnostic knowledge acquisition. We design and construct a fault diagnosis expert system for spacecraft based on this diagnosis method. An experiment is presented to prove the effectiveness and practicality of the expert system.

A Fault Diagnosis Technique for Water-Steam Chemistry Process in Power Plant

2012 ◽  
Vol 433-440 ◽  
pp. 6467-6472
Author(s):  
Shun An Cao ◽  
Jia Yuan Hu ◽  
Yan Huang ◽  
Jian Li Xie
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Credibility Theory ◽  
Steam Quality ◽  
Data Mining Technique ◽  
Water Steam ◽  
Mining Technique ◽  
Diagnosis Method ◽  
Fuzzy Diagnosis

Carrying out the fault diagnosis of water-steam chemistry process in power plant has an important significance to ensuring high qualified rates of water and steam quality as well as maintaining safe operation of units. This paper proposed a fault diagnosis method based on improved credibility theory which is used to construct fuzzy diagnosis rules and data mining technique used to determine symptom weights and rule limens of reliability rules, and also improved the setting method of rule confidence. The adoption of data mining technique and new setting method of rule confidence can solve the problem that credibility reasoning results are influenced by person’s subjective factors, and make the reasoning process more scientific. Example results prove that this diagnosis model has a high accuracy, which indicates the significant practical value of the model.

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.

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

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