LAPSE:2023.26104
Published Article
LAPSE:2023.26104
Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach
March 31, 2023
Fault diagnosis techniques can be classified into passive and active types. Passive approaches only utilize the original input and output signals of the system. Because of the small amplitudes, the characteristics of incipient faults are not fully represented in the data of the system, so it is difficult to detect incipient faults by passive fault diagnosis techniques. In contrast, active methods can design auxiliary signals for specific faults and inject them into the system to improve fault diagnosis performance. Therefore, active fault diagnosis techniques are utilized in this article to detect and isolate incipient faults based on the fault structure. A new framework based on observer approach for active fault diagnosis is proposed and the geometric approach based fault diagnosis observer is introduced to active fault diagnosis for the first time. Based on the dynamic equations of residuals, auxiliary signals are designed to enhance the diagnosis performance for incipient faults that have specific structures. In addition, the requirements that auxiliary signals need to meet are discussed. The proposed method can realize the seamless combination of active fault diagnosis and passive fault diagnosis. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach, and it is indicated that the proposed method significantly improves the accuracy of the diagnosis for incipient faults.
Keywords
active fault diagnosis, auxiliary input, fault structure, geometric approach, incipient faults
Suggested Citation
Zhang Z, He X. Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach. (2023). LAPSE:2023.26104
Author Affiliations
Zhang Z: Department of Automation, Tsinghua University, Beijing 100084, China [ORCID]
He X: Department of Automation, Tsinghua University, Beijing 100084, China [ORCID]
Journal Name
Energies
Volume
13
Issue
17
Article Number
E4475
Year
2020
Publication Date
2020-08-31
Published Version
ISSN
1996-1073
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Original Submission
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PII: en13174475, Publication Type: Journal Article
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LAPSE:2023.26104
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doi:10.3390/en13174475
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