Application of Neural Network for Forecasting Reliability of Vacuum Equipment

Panfilova, E. V.; Sidorova, S. V.; Shramko, Darya Y.

doi:10.1109/rusautocon.2019.8867815

Cited by 5 publications

(2 citation statements)

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“…In some cases, students use data from previously performed in the laboratory experimental studies to do the homework. It was such data that was used to solve the problem of predicting the reliability of the elements of vacuum equipment [3]. Prediction of reliability indicators, in particular, mean time between failures or mean time to failure (MTTF), can be carried out at the design stage (when developing a technical specification, comparing options at the technical proposal stage and when performing a preliminary design) and during operation.…”

Section: Examples Of Student Workmentioning

confidence: 99%

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Skill Oriented Online Master’s Course “Neural Network Modeling of Complex Technical Systems”

Panfilova

2020

ITM Web Conf.

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In this paper we consider the application of online course “Neural Network modeling of Complex Technical Systems” in the Master’s degree programs in the field of nanotechnology and nanoengineering in Bauman Moscow State Technical University. The course has rather practical than theoretical nature. The aim of this course is skill oriented learning. Nowadays neural network models have become a powerful tool of scientific research for engineers and students. The methods studied during the study of the discipline can be applied to estimation, modeling, classification, clustering, forecasting and more. The neural networks modeling plays a significant role in Master’s education and student’s research work. Neural Networks models are successfully presented in graduation theses. Thanks to online educations students can practice at their own pace and study modern neural networks software products, methods of data preparing, designing and training neural network and then apply these algorithms in practice. According to the steps of neural network modeling algorithm the course consists of three main parts and conclusive one. In this paper course structure and study results are presented.

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Section: Examples Of Student Workmentioning

confidence: 99%

“…Neural networks can be used in technological process control, failure analysis, machine vision, signal identification, etc. [1,2,3].…”

Section: Introductionmentioning

confidence: 99%