Large margin-learning methodology from time-frequency maps and its physically interpretable weights for simultaneous machine health monitoring and fault diagnosis-Reference-Cited by-同舟云学术

Large margin-learning methodology from time-frequency maps and its physically interpretable weights for simultaneous machine health monitoring and fault diagnosis

Published:2023-10 Issue: Volume:200 Page:110615
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Yan Tongtong,Wang Dong,Kong Jinzhen,Peng Zhongxiao^ORCID

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Shanghai Jiao Tong University

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference49 articles.

1. A review on machinery diagnostics and prognostics implementing condition-based maintenance;Jardine;Mechanical Systems and Signal Processing,2006

2. Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion;Yan;International Journal of Aerospace Engineering,2021

3. Machinery health prognostics: A systematic review from data acquisition to RUL prediction;Lei;Mechanical Systems and Signal Processing,2018

4. Data-Driven Incipient Fault Detection and Diagnosis for the Running Gear in High-Speed Trains;Cheng;IEEE Transactions on Vehicular Technology,2020

5. A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching;Mao;IEEE Transactions on Instrumentation and Measurement,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advancements in bearing remaining useful life prediction methods: a comprehensive review;Measurement Science and Technology;2024-06-10

2. Enhancing Reliability Through Interpretability: A Comprehensive Survey of Interpretable Intelligent Fault Diagnosis in Rotating Machinery;IEEE Access;2024