Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
cетевое издание
issn 2310-6018


Sai V.С.  

UDC 004.02
DOI: 10.26102/2310-6018/2019.27.4.011

  • Abstract
  • List of references
  • About authors

At present, deep neural networks are becoming one of the most popular approaches in solving various practical problems from a wide variety of fields, such as image and speech recognition, natural language processing, computer vision, medical informatics, etc. The article considers the possibility of using deep neural networks in the implementation of proactive maintenance strategy – predictive maintenance (PdM). Various methods of constructing predictive models for PdM are considered. Currently, the data-driven approaches using deep neural networks for constructing predictive models for PdM are most promising methods. One of the reasons for the successful application of deep neural networks is that the networks automatically selects important features from the data needed to solve the problem. The most commonly used neural networks for PdM are considered: Long short-term memory (LSTM), convolutional neural networks (CNN) and autoencoders. An overview of powerful frameworks for the design and training of neural networks is given.

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Sai Van Сuong

Email: svcuonghvktqs@gmail.com

Volgograd State Technical University

Volgograd, Russian Federation

Keywords: predictive maintenance, data-driven methods, deep neural networks, lstm, cnn, autoencoders

For citation: Sai V.С. DEEP NEURAL NETWORKS FOR PREDICTIVE MAINTENANCE. Modeling, Optimization and Information Technology. 2019;7(4). Available from: https://moit.vivt.ru/wp-content/uploads/2019/11/SaiVanCuong_4_19_1.pdf DOI: 10.26102/2310-6018/2019.27.4.011 (In Russ).


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