With the increasing of the installed capacity of wind power,the condition monitoring and maintains technique is becoming more important.Wind Turbines(WT)gearbox is one of the key wind power components as it plays the ...With the increasing of the installed capacity of wind power,the condition monitoring and maintains technique is becoming more important.Wind Turbines(WT)gearbox is one of the key wind power components as it plays the role of power transmission and speed regulation.Towards this,a number of scholars have pay attention to the fault diagnosis of WT gearbox.The efficiency of Machine Learning(ML)algorithms is highly correlated with signal type,data quality,and extracted features employed.The implementation of ML techniques has proven to be advantageous in simplifying the comprehension prerequisites for fault diagnosis technology concerning fault mechanisms.More and more current studies predominantly concentrate on the utilization and fine-tuning of ML algorithms,while providing limited insights into the features of the acquired data.Therefore,it is necessary to review the research in recent years from the perspective of the combination of feature extraction and ML algorithms,and provide a detailed direction for future WT gearbox fault diagnosis technology research.In this paper,data processing algorithms and typical fault diagnosis methods based on ML methods for WT gearbox are reviewed.For the using of ML method in WT gearbox fault diagnosis,the data prepared for training is very important.The paper firstly reviewed the data analysing method which will support the ML method.The data analysing methods include data acquisition,data preprocessing and feature extraction method.Feature extraction plays a pivotal role in the realm of gearbox fault diagnosis,as it serves as the essence of effective detection.This review will primarily focus on exploring methods that enable the utilization of efficient features in combination with ML techniques to achieve accurate gearbox fault diagnosis.Then typical ML method for WT gearbox fault diagnosis are carefully reviewed.Moreover,some prospects for future research directions are discussed in the end.展开更多
基金supported by the National Key R&D Program of China(Grant No.2020YFB1709800)Open Fund of the Intelligent Green Manufacturing Technology and Equipment Collaborative Innovation center of Shandong Province(IGSD-2020-017)+1 种基金ten thousand people plan project of Zhejiang ProvinceThe“Qizhen Program”of Zhejiang University.
文摘With the increasing of the installed capacity of wind power,the condition monitoring and maintains technique is becoming more important.Wind Turbines(WT)gearbox is one of the key wind power components as it plays the role of power transmission and speed regulation.Towards this,a number of scholars have pay attention to the fault diagnosis of WT gearbox.The efficiency of Machine Learning(ML)algorithms is highly correlated with signal type,data quality,and extracted features employed.The implementation of ML techniques has proven to be advantageous in simplifying the comprehension prerequisites for fault diagnosis technology concerning fault mechanisms.More and more current studies predominantly concentrate on the utilization and fine-tuning of ML algorithms,while providing limited insights into the features of the acquired data.Therefore,it is necessary to review the research in recent years from the perspective of the combination of feature extraction and ML algorithms,and provide a detailed direction for future WT gearbox fault diagnosis technology research.In this paper,data processing algorithms and typical fault diagnosis methods based on ML methods for WT gearbox are reviewed.For the using of ML method in WT gearbox fault diagnosis,the data prepared for training is very important.The paper firstly reviewed the data analysing method which will support the ML method.The data analysing methods include data acquisition,data preprocessing and feature extraction method.Feature extraction plays a pivotal role in the realm of gearbox fault diagnosis,as it serves as the essence of effective detection.This review will primarily focus on exploring methods that enable the utilization of efficient features in combination with ML techniques to achieve accurate gearbox fault diagnosis.Then typical ML method for WT gearbox fault diagnosis are carefully reviewed.Moreover,some prospects for future research directions are discussed in the end.
