A Predictive Maintenance Model Using Recurrent Neural Networks

被引：13

作者：

Rivas, Alberto ^{[1
]}

Fraile, Jesus M. ^{[1
]}

Chamoso, Pablo ^{[1
]}

Gonzalez-Briones, Alfonso ^{[1
]}

Sitton, Ines ^{[1
]}

Corchado, Juan M. ^{[1
,2
,3
,4
]}

机构：

[1] Univ Salamanca, BISITE Res Grp, Edificio I D I,Calle Espejo 2, Salamanca 37007, Spain

[2] IoT Digital Innovat Hub Spain, Air Inst, Salamanca 37188, Spain

[3] Osaka Inst Technol, Fac Engn, Dept Elect Informat & Commun, Osaka 5358585, Japan

[4] Univ Malaysia Kelantan, Pusat Komputeran & Informat, Karung Berkunci 36, Kota Baharu 16100, Kelantan, Malaysia

来源：

14TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2019) | 2020年 / 950卷

关键词：

Remaining useful life; Recurrent neural network; Predictive maintenance; Industry; 4.0;

D O I：

10.1007/978-3-030-20055-8_25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One of the main goals of Industry 4.0 is to anticipate machine breakdowns. Being able to prevent failures is important because downtime implies high cost and production loss. For this reason, the calculation of the number of remaining cycles or Remaining Useful Life (RUL) until a breakdown occurs is essential for machine maintenance. The calculation of the RUL should be based on previous observations, if possible under the same conditions. Research on RUL estimation has become central to the development of systems that monitor the current state of machines. Although this field has been studied in-depth, there is no single universal method. The lack of a universal method is the motivation behind this proposal in which the designed system uses recurrent neural networks (RNN) in a predictive maintenance problem.

引用

页码：261 / 270

页数：10

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