Dynamic Load Identification of Thin-Walled Cabin Based on CNN-LSTM-SA Neural Network.

Jun Wang, Shaowei Song, Chang Liu, Yali Zhao

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Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Jun WangNational Key Laboratory of Aerospace Liquid Propulsion, Xi'an Aerospace Propulsion Institute, Xi'an 710100, China.

Shaowei SongNational Key Laboratory of Aerospace Liquid Propulsion, Xi'an Aerospace Propulsion Institute, Xi'an 710100, China.

Chang LiuNational Key Laboratory of Aerospace Liquid Propulsion, Xi'an Aerospace Propulsion Institute, Xi'an 710100, China.

Yali ZhaoState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spacecraft are subjected to various external loads during flight, and these loads have a direct impact on the structural safety and functional stability of the spacecraft. Obtaining external load information can provide reliable support for spacecraft health detection and fault warning, so accurate load identification is very important for spacecraft. Compared with the traditional time-domain load identification method, the neural network-based time-domain load identification method can avoid the establishment of the inverse model and realize the response-load time-sequence mapping, which has a broad application prospect. In this paper, a CNN-LSTM-SA neural network-based load identification method is proposed for load acquisition of a thin-walled spacecraft model. Simulation results show that the method has higher identification accuracy and robustness (RMSE and MAE of 8.47 and 10.83, respectively, at a 20% noise level) in the load identification task compared to other network structures. The experimental results show that the coefficients of determination (R

Indexed as

CNN-LSTM-SAload identificationthin-walled compartmenttime sequence mapping

Identifiers

PMID40141538

DOI10.3390/ma18061255

PMCPMC11944270

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LicenceCC BY

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.