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Multi-label spacecraft electrical signal classification method based on DBN and random forest

Author

Listed:
  • Ke Li
  • Nan Yu
  • Pengfei Li
  • Shimin Song
  • Yalei Wu
  • Yang Li
  • Meng Liu

Abstract

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

Suggested Citation

  • Ke Li & Nan Yu & Pengfei Li & Shimin Song & Yalei Wu & Yang Li & Meng Liu, 2017. "Multi-label spacecraft electrical signal classification method based on DBN and random forest," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-19, May.
    • Handle: RePEc:plo:pone00:0176614
    DOI: 10.1371/journal.pone.0176614
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    References listed on IDEAS

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    1. Ke Li & Yi Liu & Quanxin Wang & Yalei Wu & Shimin Song & Yi Sun & Tengchong Liu & Jun Wang & Yang Li & Shaoyi Du, 2015. "A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-16, November.
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    Cited by:

    1. Diya Zhang & Jiake Leng & Xianju Li & Wenxi He & Weitao Chen, 2022. "Three-Stream and Double Attention-Based DenseNet-BiLSTM for Fine Land Cover Classification of Complex Mining Landscapes," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    2. Yilmazer, Seckin & Kocaman, Sultan, 2020. "A mass appraisal assessment study using machine learning based on multiple regression and random forest," Land Use Policy, Elsevier, vol. 99(C).

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