首页|Study Results from Shanghai Dianji University Broaden Understanding of Intellige nt Systems (A Novel Local Feature Fusion Architecture for Wind Turbine Pitch Fau lt Diagnosis With Redundant Feature Screening)
Study Results from Shanghai Dianji University Broaden Understanding of Intellige nt Systems (A Novel Local Feature Fusion Architecture for Wind Turbine Pitch Fau lt Diagnosis With Redundant Feature Screening)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning-Intelligent Systems. According to news reporting originating in Shanghai, People's Republic of China, by NewsRx journalists, research stated, "The safe and reliable operation of the pitch system is essential for the stabl e and efficient operation of a wind turbine (WT). The pitch fault data collected by supervisory control and data acquisition systems (SCADA) often contain a wid e variety of variables, leading to redundant features that interfere with the ac curacy of final diagnosis results, making it difficult to meet requirements." Financial supporters for this research include European Union (EU), Royal Societ y, Alexander von Humboldt Foundation, BRIEF Award of Brunel University London in the UK, Capacity Building Project of Shanghai Local Colleges and Universities o f China.
ShanghaiPeople's Republic of ChinaAs iaIntelligent SystemsMachine LearningShanghai Dianji University
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