Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Zuqiang Su, Baoping Tang, Ziran Liu, Yi Qin. Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing, 157:208-222, 2015. [doi]

The following publications are possibly variants of this publication:

A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector MachineKe Li, Rui Zhang, Fucai Li, Lei Su, Huaqing Wang, Peng Chen 0002. access, 7:26571-26580, 2019. [doi]

Weak fault diagnosis of rotating machinery based on feature reduction with Supervised Orthogonal Local Fisher Discriminant AnalysisFeng Li, Jiaxu Wang, Minking K. Chyu, Baoping Tang. ijon, 168:505-519, 2015. [doi]

An Incremental Feature Learning Algorithm Based on Least Square Support Vector MachineXinwang Liu, Guomin Zhang, Yubin Zhan, En Zhu. faw 2008: 330-338 [doi]

Least Squares Support Vector Machine Based Partially Linear Model IdentificationYoufeng Li, Lijuan Li, Hongye Su, Jian Chu. icic 2006: 775-781 [doi]

Application of Least Squares Support Vector Machine in Fault DiagnosisYongli Zhang, Yanwei Zhu, Shufei Lin, Xiaohong Liu. icica 2011: 192-200 [doi]

Orthogonal discriminant linear local tangent space alignment for face recognitionYongzhou Li, Dayong Luo, Shaoqiang Liu. ijon, 72(4-6):1319-1323, 2009. [doi]

runs on WebDSL