Research of Intelligent Diagnostic Technology

Wei Xu; Zhao Ming Liu

doi:10.4028/www.scientific.net/AMM.321-324.1890

Paper Titles

Development and Application of Real-Time Quality Monitoring System of Hot Mixed Asphalt Mixture
p.1863

Image Classification Based on Universal Combination Operation Model
p.1869

An Early-Warning Methodology of Work safety for Chemical Industry Park Based on Immune Mechanism
p.1873

Research of Equipment Comprehensive Diagnostic Technology
p.1886

Research of Intelligent Diagnostic Technology
p.1890

Assessment Model of Accident Emergency Management System Performance for Chemical Industrial Park Based on Immune Mechanism
p.1894

Prediction Model of Passenger Waiting Time in High-Speed Rail Hub Based on Bp Neural Network
p.1903

The Intelligent Diagnostic System of Vegetable Diseases Based on a Fuzzy Neural Network
p.1907

Multi-Class Logistic Regression Classifier with BFGS Method
p.1912

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324Research of Intelligent Diagnostic Technology

Research of Intelligent Diagnostic Technology

Abstract:

In modern industrial enterprises, the level of systematization and automation keeps improving. In light of the complicated conditions that involve multiple factors, multiple failures and multiple processes in the operation of the equipments and by taking fault diagnosis of the unfavorable signs of commutation for DC motors as an example, the paper discusses the principles and application of the mixed integrated intelligent diagnosis system, taking advantage of the knowledge-based expert system and the intelligent diagnosis system of neural network ensemble.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

1890-1893

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.1890

Citation:

Cite this paper

Online since:

June 2013

Authors:

Wei Xu, Zhao Ming Liu

Keywords:

Equipment, Expert System, Intelligent Diagnosis, Neural Network (NN)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] X.S. Mu, "Development trend of equipment diagnosis technology", 2001 (2), pp.44-47.

Google Scholar

[2] D.C. Tong, "Human intelligent and intelligent diagnosis technology", Mechatronics, 2000 (6), ppt. 67-71.

Google Scholar

[3] S.Simani,C.Fantuzzi.Fault diagnosis in power plant using neural networks Information Sciences[J].2000(127):125-136.

DOI: 10.1016/s0020-0255(00)00034-7

Google Scholar

[4] Roztoc il J,Nová k M. Fault diagnosis of rotating machinery based on wavelet transforms and neural network[C].2010 International Conference on Applied Electronics,2010:1-6.

Google Scholar

[5] Sun Jianghong,Zuo Yunbo. Single Sensor Information Fusion for Local Fault Prediction of Large Rotating Machinery[C].2010 International Conference on Electrical and Control Engineering , 2010:1950-1953..

DOI: 10.1109/icece.2010.479

Google Scholar