TY - GEN
T1 - FD-ELM Diagnosis Method for Electric Actuator of Six-Degree-of-Freedom Motion Platform
AU - Liu, Dawei
AU - Zuo, Yakun
AU - Wang, Zhipeng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The existing fault diagnosis methods based on the neural network have some problems, such as inaccurate diagnosis positioning and insufficient manual feature extraction in the variable working conditions of the electric actuator of the six-degree-of-freedom motion platform. To solve these problems, this paper proposed a diagnosis method of the electric actuator based on the fixed dictionary-extreme learning machine (FD-ELM):1. The variational mode decomposition (VMD) decomposed the sensor signals.2.The singular value decomposition (SVD) algorithm extracted the features of the modes decomposed by VMD to weaken the influence of noise.3.FD-ELM adaptive to variable speed is used to learn the extracted features, and the learned FD-ELM algorithm model completes the fault identification of the electric actuator. Experimental results show that the proposed method can effectively identify the faults of the electric actuator under varying working conditions. In addition, the results of the proposed method are better than the existing neural network fault diagnosis methods in the comparison experiment based on the vibration data set of the real six-degree-of-freedom motion platform of the full-function train driving simulator.
AB - The existing fault diagnosis methods based on the neural network have some problems, such as inaccurate diagnosis positioning and insufficient manual feature extraction in the variable working conditions of the electric actuator of the six-degree-of-freedom motion platform. To solve these problems, this paper proposed a diagnosis method of the electric actuator based on the fixed dictionary-extreme learning machine (FD-ELM):1. The variational mode decomposition (VMD) decomposed the sensor signals.2.The singular value decomposition (SVD) algorithm extracted the features of the modes decomposed by VMD to weaken the influence of noise.3.FD-ELM adaptive to variable speed is used to learn the extracted features, and the learned FD-ELM algorithm model completes the fault identification of the electric actuator. Experimental results show that the proposed method can effectively identify the faults of the electric actuator under varying working conditions. In addition, the results of the proposed method are better than the existing neural network fault diagnosis methods in the comparison experiment based on the vibration data set of the real six-degree-of-freedom motion platform of the full-function train driving simulator.
KW - Fault Diagnosis
KW - Fixed Dictionary-Extreme Learning Machine
KW - Full-function Train Driving Simulator
KW - Singular Value Decomposition
KW - Variational Modal Decomposition
UR - https://www.scopus.com/pages/publications/85201940438
U2 - 10.1007/978-981-97-3682-9_76
DO - 10.1007/978-981-97-3682-9_76
M3 - 会议稿件
AN - SCOPUS:85201940438
SN - 9789819736812
T3 - Lecture Notes in Electrical Engineering
SP - 829
EP - 839
BT - Developments and Applications in SmartRail, Traffic, and Transportation Engineering - Proceedings of ICSTTE 2023
A2 - Jia, Limin
A2 - Qin, Yong
A2 - Easa, Said
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on SmartRail, Traffic, and Transportation Engineering, ICSTTE 2023
Y2 - 28 July 2023 through 30 July 2023
ER -