Naslov
Rotating Shaft Fault Prediction Using Convolutional Neural Network: A Preliminary Study

Autori
Kolar, Davor ; Lisjak, Dragutin ; Pająk, Michał

Izvornik
Journal of KONES (1231-4005) 26 (2019), 3; 75-81

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
condition-based maintenance, rotating systems, fault diagnosis, convolutional neural networks

Sažetak
Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for both expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This article presents authors initial research in deep learning-based data-driven fault diagnosis of rotating subsystems. The proposed technique input raw three-axis accelerometer signal as high-definition image into deep learning layers, which automatically extract signal features, enabling high classification accuracy.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo, Tehnologija prometa i transport

POVEZANOST RADA