Linear maximum margin tensor classification based on flexible convex hulls for fault diagnosis of rolling bearings
作者：
Highlights：
• A new tensor classifier called MMTC-FCH is proposed and applied to the fault diagnosis of rolling bearings.
• CP decomposition has been applied to the calculation process of tensor inner product.
• The reduction factor is used to improve the robustness of MMTC-FCH.
• Wavelet time–frequency grayscale images are used as second-order feature tensors.
• Experimental results show that MMTC-FCH is superior to the classifiers of the vector space.
摘要
•A new tensor classifier called MMTC-FCH is proposed and applied to the fault diagnosis of rolling bearings.•CP decomposition has been applied to the calculation process of tensor inner product.•The reduction factor is used to improve the robustness of MMTC-FCH.•Wavelet time–frequency grayscale images are used as second-order feature tensors.•Experimental results show that MMTC-FCH is superior to the classifiers of the vector space.
论文关键词：Rolling bearings,Fault diagnosis,Support tensor machine,Maximum margin tensor classification,Flexible convex hull
论文评审过程：Received 25 September 2018, Revised 12 February 2019, Accepted 20 February 2019, Available online 23 February 2019, Version of Record 21 March 2019.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.02.024
