Research on fault diagnosis method of control moment gyroscope based on K-means algorithm

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated, and resolved in time. Fault diagnosis provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, it can avoid failure and improve the satellite reliability. In this paper, an enhanced data-driven fault diagnosis is introduced for fault isolation of multiple in-phase faults of satellite control moment gyroscopes that has not been addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine, and the results yield fault predictions with up to 95.6% accuracy. In addition, a sensitivity analysis with regard to noise, missing values, and missing sensors is done. The results show that the proposed model is robust enough to be used in real applications.

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Fault Diagnosis of Control Moment Gyroscope Using Optimized Support Vector Machine

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc42975.2020.9283402 ◽

2020 ◽

Author(s):

Hossein Varvani Farahani ◽

Afshin Rahimi

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Support Vector ◽

Control Moment Gyroscope ◽

Control Moment

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Fault Diagnosis Method for Marine Hydraulic System

ICLEM 2012 ◽

10.1061/9780784412602.0104 ◽

2012 ◽

Author(s):

Changwei He ◽

Dawei Liu

Keyword(s):

Fault Diagnosis ◽

Hydraulic System ◽

Diagnosis Method

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Helicopter main reduction planetary gear fault diagnosis method based on SVDD

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209316 ◽

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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Towards High Accuracy Fault Diagnosis of Digital Circuits

ISTFA 2004: Conference Proceedings from the 30th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2004p0047 ◽

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.

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Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

Recent Patents on Engineering ◽

10.2174/1872212113666191121122720 ◽

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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