Generator Fault Diagnosis with Bit-Coding Support Vector Regression Algorithm

Generator fault diagnosis has a great impact on power networks. With the coupling effects, some uncertain factors, and all the complexities of generator design, fault diagnosis is difficult using any theoretical analysis or mathematical model. This paper proposes a bit-coding support vector regressi...

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Vydáno v:	Energies (Basel) Ročník 16; číslo 8; s. 3582
Hlavní autor:	Lin, Whei-Min
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.04.2023
Témata:	Algorithms bit-coding support vector regression (BSVR) Classification Comparative analysis Design Fault diagnosis Fuzzy logic generator fault diagnosis (GFD) Neural networks support vector machine (SVM) support vector regression (SVR) Turbines Vibration
ISSN:	1996-1073, 1996-1073
On-line přístup:	Získat plný text
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Shrnutí:	Generator fault diagnosis has a great impact on power networks. With the coupling effects, some uncertain factors, and all the complexities of generator design, fault diagnosis is difficult using any theoretical analysis or mathematical model. This paper proposes a bit-coding support vector regression (BSVR) algorithm for turbine generator fault diagnosis (GFD) based on a support vector machine (SVM) capable of processing multiple classification problems of fault diagnosis. The BSVR can simplify the design architecture and reduce the processing time for detection, where m classifier is needed for m class problems compared to the [m(m − 1)]/2 size of the original multi-class SVM. Compared with conventional methods, numerical test results showed a high accuracy, good robustness, and a faster processing performance.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1996-1073 1996-1073
DOI:	10.3390/en16083582