Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery

Applied Sciences

卷:11

Jiantao Lu¹  Rongqing Cui¹  Shunming Li¹  Weiwei Qian² 

[1] College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

[2] School of Artificial Intelligence, Nanjing University of Information Sciences and Technology, Nanjing 210016, China;

关键词: intelligent fault diagnosis;    rotating machinery;    correlation vector;    k-nearest neighbor;    sparse filtering;    sparse coding;   

DOI  :  10.3390/app11030919

来源: DOAJ

【 摘 要 】

Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method.

【 授权许可】

Unknown