Your English writing platform
Discover LudwigSuggestions(2)
Exact(60)
The proposed method is applied to analyze the simulation, gearbox and rolling element bearing vibration signals.
Long fatigue life is the most important objective in the optimum design of rolling element bearing.
Time and frequency domain analyses are performed for diagnostics of rolling element bearing structures.
It outperforms AULW in detection both of an inner race fault and an outer race fault of a rolling element bearing and it outperforms MUDW in detection of an outer race fault of a rolling element bearing.
This paper presents a novel signal processing scheme, adaptive morphological update lifting wavelet (AMULW), for rolling element bearing fault detection.
Periodic impulses arise due to localised defects in rolling element bearing.
With these properties, the proposed weak signal detection strategy is indicated to be beneficial to rolling element bearing fault diagnosis.
The sensitive feature extraction from vibration signals is still a great challenge for effective fault classification of rolling element bearing.
This paper presents a novel signal processing scheme, namely time-varying morphological filtering (TMF), for rolling element bearing fault detection.
In this paper, a modified nonlocal means denoising (NL-means) algorithm is proposed for rolling element bearing fault diagnosis.
The MUDW scheme is used to extract impulse features from rolling element bearing defect signals imposed with noise.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com