TY - GEN
T1 - Deep Neural Network Based Suspension Control Failure Identification of Maglev Systems
AU - Jiang, Gao Feng
AU - Wang, Su Mei
AU - Ni, Yi Qing
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The comfort and stability of maglev systems are crucial to passenger safety and riding experience. Among them, the suspension gap between the bogie and the rail is one of the critical factors that determine comfort and stability. However, the structural disturbance may cause unstable control of the suspension gap. To address this problem, this study aims to analyze and identify the suspension control failure in a deep neural network approach. An array of accelerometers is installed on the rail to capture the structural response caused by suspension control failure. With the collected acceleration data, a neural network-based classification algorithm is proposed to distinguish the normal and failed suspension control status. As a result, the feature pattern between the normal and failed suspension control can be found in the collected acceleration data, and the proposed algorithm can reasonably identify the suspension control failure. Therefore, this study will help improve the failure identification of maglev systems, as well as the reliability and safety of maglev systems and the comfortable ride experience for passengers.
AB - The comfort and stability of maglev systems are crucial to passenger safety and riding experience. Among them, the suspension gap between the bogie and the rail is one of the critical factors that determine comfort and stability. However, the structural disturbance may cause unstable control of the suspension gap. To address this problem, this study aims to analyze and identify the suspension control failure in a deep neural network approach. An array of accelerometers is installed on the rail to capture the structural response caused by suspension control failure. With the collected acceleration data, a neural network-based classification algorithm is proposed to distinguish the normal and failed suspension control status. As a result, the feature pattern between the normal and failed suspension control can be found in the collected acceleration data, and the proposed algorithm can reasonably identify the suspension control failure. Therefore, this study will help improve the failure identification of maglev systems, as well as the reliability and safety of maglev systems and the comfortable ride experience for passengers.
KW - deep neural networks
KW - maglev systems
KW - suspension control
KW - suspension failure detection
UR - http://www.scopus.com/inward/record.url?scp=85182739448&partnerID=8YFLogxK
U2 - 10.1109/CSIS-IAC60628.2023.10363796
DO - 10.1109/CSIS-IAC60628.2023.10363796
M3 - Conference article published in proceeding or book
AN - SCOPUS:85182739448
T3 - 2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023
SP - 971
EP - 976
BT - 2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023
Y2 - 20 October 2023 through 22 October 2023
ER -