Fault Diagnosis of Wind Turbine Bolts based on ICEEMD-SSA-SVM Model DOI

Qianhua Ge,

Dexing Wang,

Kai Sun

и другие.

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), Год журнала: 2023, Номер 17(3), С. 269 - 282

Опубликована: Июль 6, 2023

Background: Compared with traditional power generation systems, wind turbines have more units and work in a harsh environment, thus relatively high failure rate. Among blade faults, the faults of high-strength bolts are often difficult to detect need be analyzed high-precision sensors other equipment. However, there is still little research on faults. Methods: The improved complete ensemble empirical mode decomposition (ICEEMD) model used extract fault features from time series data, then combined support vector machine optimized by sparrow search algorithm (SSA-SVM) diagnose bolt different degrees, so as achieve purpose early warning. Results: results show that ICEEMD this paper can signals well, SSA-SVM has shorter optimization accurate classification compared models such PSO-SVM. Conclusion: hybrid proposed important for diagnosis operation monitoring class.

Язык: Английский

Corrosion Failure Analysis of Zirconium Metal Crucible for Sn-Content Testing at Tin Ore Smelting Industry DOI

Shokhul Lutfi,

Muhammad Waziz Wildan,

Muhammad Robby Firmansyah

и другие.

Опубликована: Янв. 1, 2024

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Язык: Английский

Процитировано

0
Fault Diagnosis of Wind Turbine Bolts based on ICEEMD-SSA-SVM Model DOI

Qianhua Ge,

Dexing Wang,

Kai Sun

и другие.

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), Год журнала: 2023, Номер 17(3), С. 269 - 282

Опубликована: Июль 6, 2023

Background: Compared with traditional power generation systems, wind turbines have more units and work in a harsh environment, thus relatively high failure rate. Among blade faults, the faults of high-strength bolts are often difficult to detect need be analyzed high-precision sensors other equipment. However, there is still little research on faults. Methods: The improved complete ensemble empirical mode decomposition (ICEEMD) model used extract fault features from time series data, then combined support vector machine optimized by sparrow search algorithm (SSA-SVM) diagnose bolt different degrees, so as achieve purpose early warning. Results: results show that ICEEMD this paper can signals well, SSA-SVM has shorter optimization accurate classification compared models such PSO-SVM. Conclusion: hybrid proposed important for diagnosis operation monitoring class.

Язык: Английский

Процитировано

0