A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data

A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data

Rotating machinery is widely used across various industries, making its reliable operation crucial for industrial production. However, in real-world settings, intelligent fault diagnosis faces challenges due to imbalanced fault data and the complexity of neural network models. These challenges are p...

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Bibliographic Details
Main Authors: Wenhao Lu, Wei Wang, Xuefei Qin, Zhiqiang Cai
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
rotating machinery
fault diagnosis
imbalanced data
online machine monitoring
lightweight neural network
Online Access:https://www.mdpi.com/2076-3417/14/24/11910
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