A Novel Deep Learning Model for Mechanical Rotating Parts Fault Diagnosis Based on Optimal Transport and Generative Adversarial Networks
| Autor: | Qie Youtian, Ping Song, Wang Xuanquan, Yifan Li, Xiongjun Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
TK1001-1841
Control and Optimization Computer science Distributed computing 02 engineering and technology Fault (power engineering) 01 natural sciences Adversarial system Production of electric energy or power. Powerplants. Central stations 0202 electrical engineering electronic engineering information engineering rotating component Architecture Materials of engineering and construction. Mechanics of materials Network architecture business.industry Deep learning 020208 electrical & electronic engineering 010401 analytical chemistry Process (computing) capsule network Transmission system fault diagnosis 0104 chemical sciences optimal transport Control and Systems Engineering TA401-492 Artificial intelligence generative adversarial networks business Generative grammar |
| Zdroj: | Actuators, Vol 10, Iss 146, p 146 (2021) Actuators Volume 10 Issue 7 |
| ISSN: | 2076-0825 |
| Popis: | To solve the poor real-time performance of the existing fault diagnosis algorithms on transmission system rotating components, this paper proposes a novel high-dimensional OT-Caps (Optimal Transport–Capsule Network) model. Based on the traditional capsule network algorithm, an auxiliary loss is introduced during the offline training process to improve the network architecture. Simultaneously, an optimal transport theory and a generative adversarial network are introduced into the auxiliary loss, which accurately depicts the error distribution of the fault characteristic. The proposed model solves the low real-time performance of the capsule network algorithm due to complex architecture, long calculation time, and oversized hardware resource consumption. Meanwhile, it ensures the high precision, early prediction, and transfer aptitude of fault diagnosis. Finally, the model’s effectiveness is verified by the public data sets and the actual faults data of the transmission system, which provide technical support for the application. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|