ArticleSensors (Basel, Switzerland)2023
Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- ConvECA-Net: A lightweight convolutional neural network for fault diagnosis of tapered roller bearings.Scientific reports · 2026Article
- Vibration-Based Diagnostics of Rolling Element Bearings Using the Independent Component Analysis (ICA) Method.Sensors (Basel, Switzerland) · 2025Article
- A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model.Scientific reports · 2024Article
- A Novel Method for Rolling Bearing Fault Diagnosis Based on Gramian Angular Field and CNN-ViT.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.