Fault diagnosis of electronic manufacturing system based on probabilistic neural network

Autor:	Chunyue Huang, Xie Jun, Ying Liang, Wei Wei, Liu Shoufu
Rok vydání:	2020
Předmět:	Probabilistic neural network Identification (information) Artificial neural network Computer science Component (UML) Test set Probabilistic logic SMT placement equipment Hardware_PERFORMANCEANDRELIABILITY Data mining Fault (power engineering) computer.software_genre computer
Zdroj:	2020 21st International Conference on Electronic Packaging Technology (ICEPT).
DOI:	10.1109/icept50128.2020.9202960
Popis:	The placement machine is an important component of the SMT equipment of the electronic manufacturing system. The placement rate is an important indicator for considering the SMT production efficiency. The placement machine will always have some failures during the production process. It is very necessary to carry out accurate diagnosis. In this paper, a fault diagnosis method of placement machine based on probabilistic neural network is proposed, and a fault diagnosis model is established and compared with BP neural network. The simulation results show that the fault diagnosis model of the placement machine based on the probabilistic neural network is superior to the BP neural network in training time and diagnosis accuracy. When using the test set samples for fault diagnosis, the fault diagnosis model based on the probabilistic neural network can perform accurate fault identification.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::24e2b6d5814475ed54dc8391db2e84b8 https://doi.org/10.1109/icept50128.2020.9202960 Zobrazit plný text záznamu