Remaining Useful Life Estimation with Parallel Convolutional Neural Networks on Predictive Maintenance Applications
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Maintenance work in the industry is performed as failure based corrective maintenance and calendar based preventive maintenance strategies. These strategies cannot meet the demands of the industry in terms of maintenance costs and production efficiency. Data-based predictive maintenance strategy aim at efficiency in production and optimum point in maintenance works. This study is based on Remaining Useful Life, which is the basis of the predictive maintenance strategy. The data used in the study is the dataset of aircraft engines. The data received from many sensors of the running motor are fixed by sliding window. A new approach has been introduced in the estimation of Remaining Useful Life with the proposed Parallel Convolutional Neural Network. By defining a problem-specific asymmetric cost function, better results have been obtained in terms of sensitivity. © 2020 IEEE.
Açıklama
28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413
Anahtar Kelimeler
Convolutional Neural Network, Predictive Maintenance, Remaining Useful Life
Kaynak
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
