Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances

Fault Diagnosis Based on Multi-Scale Redefined Dimensionless Indicators and Density Peak Clustering With Geodesic Distances

A novel fault diagnosis method for rolling bearings based on multi-scale redefined dimensionless indicators and an unsupervised feature selection method using density peak clustering with geodesic distances is proposed in this paper. First, a new feature extraction method is proposed based on redefi...

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Bibliographic Details
Main Authors: Qin Hu, Qi Zhang, Xiao-Sheng Si, Ai-Song Qin, Qing-Hua Zhang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Feature extraction
unsupervised learning
nearest neighbor searches
clustering algorithms
mechanical engineering
vibration measurement
Online Access:https://ieeexplore.ieee.org/document/9075995/

Internet

https://ieeexplore.ieee.org/document/9075995/

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