Health indicator based on signal probability distribution measures for machinery condition monitoring-Reference-Cited by-同舟云学术

Health indicator based on signal probability distribution measures for machinery condition monitoring

Published:2023-09 Issue: Volume:198 Page:110460
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Zhang Guangyao,Wang Yi^ORCID,Li Xiaomeng,Qin Yi,Tang Baoping

Publisher

Elsevier BV

Subject

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

Reference50 articles.

1. Machinery health prognostics: a systematic review from data acquisition to RUL prediction;Lei;Mech. Syst. Sig. Process.,2018

2. Fisher’s discriminant ratio based health indicator for locating informative frequency bands for machine performance degradation assessment;Yan;Mech. Syst. Sig. Process.,2022

3. Adaptive weighted signal preprocessing technique for machine health monitoring;Hou;IEEE Trans. Instrum. Meas.,2021

4. Machinery health indicator construction based on convolutional neural networks considering trend burr;Guo;Neurocomputing,2018

5. Kurtosis: A critical review;Balanda;Am. Statistician,1988

Cited by 16 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. On the use of vibrations and temperatures for the monitoring of plastic chain conveyor systems;Mechanical Systems and Signal Processing;2025-01

2. Deep Koopman Operator-based degradation modelling;Reliability Engineering & System Safety;2024-11

3. On the design of Optimal Health Indicators for early fault detection and their statistical thresholds;Mechanical Systems and Signal Processing;2024-09

4. Interfering sensed input classification model using assimilated whale optimization and deep Q-learning for remote patient monitoring;Biomedical Signal Processing and Control;2024-07

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