Earticle

초록

영어

The vibration signal is nonstationary and it is difficult to acquire the sample with typical fault. An improved ACO algorithm based on adaptive control parameters is introduced into SVM model to propose a new fault diagnosis (IMASFD) method in this paper. In the IMASFD method, the EMD method is used to decompose fault vibration signal into IMF components, the energy of IMF components is selected to construct the fault feature vectors. Then the adaptive controlling pheromone strategy, adaptive controlling stochastic selection threshold strategy and dynamic evaporation rate strategy are used to improve the basic ACO algorithm. The improved ACO algorithm is used to optimize the parameters of SVM model in order to obtain the optimal values of parameter combination in the SVM model. And a new fault diagnosis (IMASFD) method is proposed. Finally, the proposed IMASFD method is applied to the test data from bearing data center of CWRU. The experimental results show that the proposed method can accurately and effectively realize high precision fault diagnosis of rolling bearing, and has strong robustness and generalization ability, provides an effective method for realizing fault diagnosis of rolling bearing.

목차

Abstract
 1. Introduction
 2. SVM Model
 3. Improved ACO algorithm
  3.1. ACO algorithm
  3.2. Improved ACO Algorithm based on Adaptive Control Parameters
 4. A Fault Diagnosis Method of Rolling Element Bearing
  4.1. The Idea of the Hybrid Method Based Improved ACO and SVM Model
  4.2. A Fault Diagnosis Method Based on the Improved ACO and SVM
 5. Bearing Fault Diagnosis Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Wu Deng [ Software Institute, Dalian Jiaotong University, Dalian 116028 China, The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044 China, Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031 China, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (Guangxi University for Nationalities), Nanning 530006 China, Provincial Key Laboratory for Computer Information Processing Technology，Soochow University, Suzhou 215006 China ]
• Xiumei Li [ Software Institute, Dalian Jiaotong University, Dalian 116028 China, The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044 China ]
• Huimin Zhao [ Software Institute, Dalian Jiaotong University, Dalian 116028 China, The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044 China, Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031 China, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (Guangxi University for Nationalities), Nanning 530006 China, Provincial Key Laboratory for Computer Information Processing Technology，Soochow University, Suzhou 215006 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605