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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Study of spacecraft fault diagnosis based on federated comparative learning

Yang Xiajie1
Lin Ziqian1
Xu Dandan1
You Yanli2
Fan Chongjun1
1. Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
2. Shanghai Jing'an District Amateur University (Shanghai Open University Jing'an Branch), Shanghai 200040, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0481

Abstract

Spacecraft fault diagnosis typically involves interpreting telemetry parameters. Time-series data collected by key spacecraft devices exhibit a Non-Independent Identically Distributed (Non-IID) pattern. Traditional federated learning methods tend to cause model drift due to local model updates when handling such data. To address this issue, the study proposed a federated optimization algorithm called Federated Contrastive Time-series Anomaly Detection (FclTAD) . The algorithm analyzed spacecraft Non-IID time-series data to detect anomalies at any given moment, enabling timely interventions. The FclTAD algorithm consisted of two stages: local update and global aggregation. The local update phase used an autoencoder to capture long-term dependencies and nonlinear relationships in the time series. It introduced a joint contrastive regularization loss to enhance the model's discriminative ability for fault diagnosis. The global aggregation phase accounted for sensor differences and applied a normalization and weighted aggregation strategy to correct inconsistent local updates. This adjustment reduced the negative impact of erroneous updates on the global model. The study conducted simulations using the NASA public datasets MSL and SMAP, as well as the Machine Prediction Server dataset (PSM) . Results showed that FclTAD improved fault diagnosis accuracy by approximately 14.37% and the adjusted F1 score by 7.82% compared to other methods. FclTAD protects spacecraft data privacy, addresses the “client drift” problem, and effectively utilizes relationships between local and global models. The method achieves excellent performance in spacecraft Non-IID time-series fault diagnosis.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0481
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-13] Accepted Paper

Cite This Article

杨夏洁, 林子谦, 徐丹丹, 等. 基于联邦对比学习的航天器故障诊断研究 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0481. (Yang Xiajie, Lin Ziqian, Xu Dandan, et al. Study of spacecraft fault diagnosis based on federated comparative learning [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0481. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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