Lin Zhu
Southeast University
5 Papers
12 Citations
Lin Zhu is an academic researcher from Southeast University. The author has contributed to research in topics: Computer science & Filter (signal processing). The author has an hindex of 3, co-authored 3 publications.
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Papers
Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method.
TL;DR: A new optimal scale morphology analysis method, named adaptive multiscale combination morphological filter-hat transform (AMCMFH), is proposed for rolling element bearing fault diagnosis, which can both reduce stochastic noise and reserve signal details.
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Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis
TL;DR: The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed and the characteristics of different algorithms are compared, and application situations of these methods are summarized.
Weighted sparsity-based denoising for extracting incipient fault in rolling bearing
TL;DR: Simulation and experimental results show that the proposed approach can successfully extract fault features from the signals of low signal to noise ratio, thus effectively improving computational efficiency.
12
Sensitivity influence of initial crack characteristics on structural damage propagation based on the VB-PCE model and POD reduced order algorithm
Lin Zhu,Junhua Wang,Jianchun Qiu,Min Chen,Min-ping Jia +4 more
TL;DR: This study proposes an improved sensitivity model using the VB-PCE model and POD algorithm to analyze the influence of initial crack characteristics on structural damage propagation, achieving 93.34% accuracy in predicting crack parameter sensitivities.
2
Approach for the structural reliability analysis by the modified sensitivity model based on response surface function - Kriging model
TL;DR: In this article , a revised sensitivity analysis model is proposed to describe the impacts of condition parameters on structural reliability and the average prediction accuracy of the quantitative structural reliability index for the influencing parameters is up to 95.91%.