Research on Fault Detection Method for Power Plant Fan Blades Based on Multimodal Data Fusion
Conventional fault detection methods for wind turbine blades in power plants mainly focus on detecting imbalanced data,and some weak fault data is not integrated into the dataset,which affects the accuracy of final fault detection,therefore,a fault detection method for power plant fan blades based on multimodal data fusion was designed.Extract abnormal characteristics of sound signals from wind turbine blades in power plants,randomly extract abnormal sound signals from wind turbine blades,and ensure the accuracy of fault detection.Building a fault detection model for wind turbine blades based on multimodal data fusion,analyzing the blade modes under operating conditions,in order to meet the accuracy requirements of blade fault detection.Set random resonance parameters for power plant fan blade faults,taking into account blade frequency range,noise intensity,random resonance time constant,etc.,to achieve the best fault detection effect.Through comparative experiments,it was verified that the fault detection accuracy of this method is higher and can be applied in practical life.
multimodal data fusionpower plantsfanblade failuredetection method
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