An adaptive threshold based on support vector machine for fault diagnosis

Considering the drawback of the big error when using fixed threshold in fault diagnosis for hydraulic servo system, many factors that may affect the fault threshold are analyzed. By integrating the key factors in threshold model, such as modeling error, random disturbance, input instructions, system...

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Bibliographic Details
Published in2009 8th International Conference on Reliability, Maintainability and Safety pp. 907 - 911
Main Authors Hongmei Liu, Chen Lu, Wenkui Hou, Shaoping Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Actuator
Adaptive threshold
Error analysis
Fault detection
Fault diagnosis
Hydraulic actuators
Hydraulic servo system
Mathematical model
Pattern recognition
Robustness
Servomechanisms
Support vector machine(SVM)
Support vector machines
Systems engineering and theory
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Summary:Considering the drawback of the big error when using fixed threshold in fault diagnosis for hydraulic servo system, many factors that may affect the fault threshold are analyzed. By integrating the key factors in threshold model, such as modeling error, random disturbance, input instructions, system current status and etc, an adaptive threshold scheme for fault diagnosis is proposed in this paper, which is based on a pattern recognition algorithm called support vector machine (SVM). It is very effective to adaptively adjust the fault threshold according to a variety of influencing factors. And the robustness is improved by the proposed method, which is verified by experimental results.
ISBN:1424449030
9781424449033
DOI:10.1109/ICRMS.2009.5269966