Multisensor Fault Diagnosis of Rolling Bearing with Noisy Unbalanced Data via Intuitionistic Fuzzy Weighted Least Squares Twin Support Higher-Order Tensor Machine

Multisensor Fault Diagnosis of Rolling Bearing with Noisy Unbalanced Data via Intuitionistic Fuzzy Weighted Least Squares Twin Support Higher-Order Tensor Machine

Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in th...

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Bibliographic Details
Main Authors: Shengli Dong, Yifang Zhang, Shengzheng Wang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Machines
Subjects:
fault diagnosis
class imbalance
intuitionistic fuzzy
twin support higher-order tensor machine
Tucker decomposition
Online Access:https://www.mdpi.com/2075-1702/13/6/445
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