Gear crack level identification based on weighted K nearest neighbor classification algorithm

By Yaguo Lei, Ming Jian Zuo. 2009

279 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/c26f187410a8ab8b9255b4c6d45bde79ab5d242e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Naipeng Li
2. Enrico Zio
3. Yaguo Lei
4. Boyuan Yang
5. Ruonan Liu
6. Jing Lin
7. Yicheng Li
8. Ya-dong Xue

In Mechanical Engineering

1. Naipeng Li
2. Yaguo Lei
3. Yicheng Li
4. Ya-dong Xue
5. Xuefeng Chen
6. Enrico Zio
7. Boyuan Yang
8. Ruonan Liu

Citations in the corpus

• A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects (2018, methodology): “…Senouci, 1995; Jin and Zhou, 2014), support vector machine (SVM) (Qu et al., 2010), Adaboost (Cord and Chambon, 2012), K-nearest neighbors algorithm (Lei and Zuo, 2009), grouping techniques (Yeum and Dyke, 2015) and Restricted Boltzmann Machine (Rafiei and Adeli, 2016, 2017; Rafiei et al., 2017)…”
• A Model-Based Method for Remaining Useful Life Prediction of Machinery (2016, background): “, ten time-domain features [38], 16 time-frequency-domain features, and two features based on trigonometric functions [34].”
• Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, methodology): “Lei and Zuo [40] propose a gear crack level identification method based on a two-stage feature selection via Euclidean distance evaluation technique and a weighted k -NN classifier.”