FOA Based Diagnosis Model for Multivariate Production Process
| Autor: | Mingshun Yang, Xiangjian Kong, Xinqin Gao, Yong Liu, Yan Li |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Multivariate statistics
Engineering Artificial neural network Computer Networks and Communications business.industry Multivariable calculus Stability (learning theory) Process (computing) Fault (power engineering) computer.software_genre Field (computer science) Production (economics) Data mining business human activities computer Software Simulation |
| Zdroj: | International Journal of u- and e-Service, Science and Technology. 8:347-356 |
| ISSN: | 2005-4246 |
| Popis: | Fault diagnosis for quality control during the multivariate production process is widely used to detect abnormal fluctuations, find out failure reasons and take measures to maintain the stability of the production system accordingly. The neural network methodology has become a main method in the field of intelligent diagnosis recently. However, it has certain deficiencies such as longer training time, slower convergence rate and easier falling into a local optimal solution easily. As a result, the effect of fault diagnose is influenced. Thus, this article proposes the idea of using the Fruit Fly Optimization Algorithm in the multivariable process fault diagnosis model, at the same time, to analyze the out of control sample data in the automobile crankshaft production. Compared with the neural network model in dealing with the fault diagnosis in multivariate process, Fruit Fly Optimization Algorithm’s effectiveness s verified. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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