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Advances in Energy Engineering (AEE) Volume 3, 2015 doi: 10.14355/aee.2015.03.002
The Fault Diagnosis of Wind Turbine Gearbox Based on Improved KNN Long Peng1, Bin Jiao2, Hai Liu3, Ting Zhang4 School of Electrical Engineering, Shanghai Dianji University, No. 1350, GanLan Road, LinGang New City, PuDong New District, Shanghai, China 375696898@qq.com; 2jiaob@sdju.edu.cn; 3642993430@qq.com; 41013753534@qq.com
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Abstract K-Nearest Neighbor Algorithm (KNN) is a fault pattern recognition method commonly used in the field of fault diagnosis. On account of the problem that the value of K is too difficult to determine in KNN algorithm—if K is too small, the classification results are susceptible to noise influence; if K is too big, near neighbor may contain too many other types of points. In this paper, we put forward an improved method. Meanwhile, we designed the experiment about the fault diagnosis of gearbox and applied experimental data to instance verification of improved KNN algorithm in Matlab. The experimental results indicated that this improved algorithm had high recognition efficiency. Key Words K-Nearest Neighbor Algorithm; Wind Turbine Gearbox; Matlab; Fault Diagnosis
Introduction Wind power is the fastest growing energy resource in the world and will be continuously developed for a long time. In the next 20 years, wind power in United States and Europe will account for 20% of the total amount of their energy [1]. The cost of the wind turbine gearbox accounts for about 30% of the whole wind turbine equipment. The wind turbine gearbox is the equipment which is easy to cause fault and lead to the shutdown of the wind turbine. The downtime of the wind turbine gearbox approximately accounts for more than 60% of the total downtime. Therefore, so many researches focus on the fault diagnosis and the monitoring of the wind power gearbox and hope to effectively reduce the generation of wind turbine fault and save the cost of wind power through effective maintenance and repair by the judgment of running state of the gearbox [2]. The process of the fault diagnosis of wind turbine gearbox is essentially the recognition process of its fault type. The process infers the current running state of the wind turbine gearbox through the extraction and classification of feature information gain from vibration signal. At present, widely applied methods of gearbox fault diagnosis are: Decision tree, K-Nearest Neighbor, Support vector machine, Bayes method, Back propagation neural network, etc [3-9]. The KNN algorithm is relatively simple. It’s easy to understand and implement without estimate parameters and training. KNN is particularly suitable for multi-modal problem, whose object has multiple class labels. For example, the performance of KNN is better than that of SVM while judging the classification of its function according to genetic characteristics. K-Nearest Neighbor Algorithm The Principle of K-Nearest Neighbor Algorithm KNN algorithm is relatively simple classification algorithm [10]. The main idea of this algorithm is to calculate differences between samples to be classified and train samples, sort these differences from small to large. Then the KNN algorithm chooses the first K categories with smallest difference, counts the category which is the most frequent in K categories as the most similar category and finally assigns samples to be classified to the most similar train samples. The process of KNN algorithm is mainly divided into five points. Firstly, calculate the distance between classification samples and train samples; Secondly, sort these distances in a certain order; Thirdly, choose corresponding categories of the first K distances; Fourthly, calculate the frequency of occurrence of corresponding
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