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Nearest Neighbor Convex Hull Tensor Classification for Gear Intelligent Fault Diagnosis Based on Multi-Sensor Signals

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成果类型:
期刊论文
作者:
Cheng, Zhengyang;Wang, Rongji*
通讯作者:
Wang, Rongji
作者机构:
[Wang, Rongji; Cheng, Zhengyang] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构:
[Wang, Rongji] C
Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Gears;Feature extraction;Fault diagnosis;Support vector machines;Intelligent sensors;Gear intelligent fault diagnosis;feature tensor;multi-sensor signals;nearest neighbor convex hull tensor classification;reduction factor
期刊:
IEEE ACCESS
ISSN:
2169-3536
年:
2019
卷:
7
页码:
140781-140793
基金类别:
This work was supported in part by the National Natural Science Foundation of China under Grant 51875183 and Grant 51575168, and in part by the Key Research and Development Program of Hunan Province under Grant 2017GK2182.
机构署名:
本校为第一且通讯机构
院系归属:
机电工程学院
摘要:
For the feature tensor of multi-sensor signals classification problem in gear intelligent fault diagnosis, a new tensor classifier named nearest neighbor convex hull tensor classification (NNCHTC) is proposed in this paper. First, the convex hull distance from a test tensor sample to the convex hull is taken as the similarity measure for classification. Then, the convex hull distance calculation is transformed into the feature tensor inner product, and CANDECOMP/PARAFAC (CP) decomposition is applied to the calculation process to capture the intrinsic information of the feature tensor. Furtherm...

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