@inproceedings{c22c17ab389c4978a65f3344c9bb9c96,
title = "Research on intelligent diagnosis of mechanical fault based on ant colony algorithm",
abstract = "Ant colony algorithm is an evolutionary optimization algorithm that simulates the foraging behavior of ant in nature, and it is distributed, parallel, robust and based on positive feedback. Basic principle of ant colony algorithm is introduced, and an adaptive clustering algorithm based on multi-ants parallel mechanism is constructed in this paper. The multi-ants parallel and adaptive clustering algorithm is applied to fault classification of locomotive wheel-paired bearings, and the accuracy rate of classification is 87\%. Research results show the algorithm is effective on practical fault diagnosis.",
keywords = "Ant colony algorithm, Clustering, Intelligent diagnosis, Supervised learning, Unsupervised learning",
author = "Zhousuo Zhang and Wei Cheng and Xiaoning Zhou",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2009.; 6th International Symposium of Neural Networks, ISNN 2009 ; Conference date: 26-05-2009 Through 29-05-2009",
year = "2009",
doi = "10.1007/978-3-642-01216-7\_67",
language = "英语",
series = "Advances in Intelligent and Soft Computing",
publisher = "Springer Verlag",
pages = "631--640",
editor = "Hongwei Wang and Yi Shen and Zhigang Zeng and Tingwen Huang",
booktitle = "Advances in Intelligent and Soft Computing",
}