A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM

Li, Ké; Liu, Yi; Wang, Quanxin; Wu, Yalei; Song, Shimin; Sun, Yi; Liu, Tengchong; Wang, Jun; Li, Yang; Du, Shaoyi

doi:10.1371/journal.pone.0140395

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…is an expected traditional VAE loss function as is introduced in (3). The deduction of (6) is given in 16 in Appendix A.…”

Section: ) Joint Probability Distributionmentioning

confidence: 99%

“…To monitor the working position and detect abnormal operations in spacecraft electronic load systems in real time, complex time-dependent electrical signals should be analyzed and recognized rapidly and accurately. In previous studies, we have assessed different machine learning methods for the fault detection of spacecraft electronic load systems, such as weighted proximal support vector machine (WPSVM) and random forest (RF) for online classification [2], [3], and fuzzy C-means (FCM) clustering for offline building of expert dataset [4].…”

Section: Introductionmentioning

confidence: 99%

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MRD-Nets: Multi-Scale Residual Networks With Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal

Liu

Zhang

et al. 2019

IEEE Access

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The fault detection of spacecraft electronic load systems is a crucial part of the spacecraft prognostics and health management system. To detect the abnormal state of spacecraft electronic load systems, complex electrical signals should be processed rapidly and accurately. For the fault detection of spacecraft electronic load systems, a robust unsupervised clustering analysis method and an accurate supervised classification method are of great importance. However, the traditional machine learning methods have poor performance when processing high-dimensional signal data because of the lack of ability to extract complex features from the signals. Therefore, neural-network-based deep learning (DL) models which can extract features from signals automatically are more suitable in this situation. In this paper, a novel convolutional neural network (CNN) module, the multi-branch residual module with dilated convolutions (MRD module), is proposed to extract multi-scale features from the electrical signal. Then, a well-designed CNN model named MRD-CNN is presented for the supervised classification task of signal. Furthermore, for the unsupervised clustering task, the clustering variational autoencoder with MRD modules (MRD-CluVAE) is proposed. The MRD-CluVAE can extract high-quality features from signal data and output the clustering results directly. To evaluate the performance of the proposed models, comparisons among the proposed models and other baseline algorithms are carried out. The experimental results show that the MRD-CNN model achieves higher classification performance and stability, while the MRD-CluVAE has a higher clustering accuracy than other algorithms. These methods can be utilized to resolve the classification and recognition problems of spacecraft electronic load signals.

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“…is an expected traditional VAE loss function as is introduced in (3). The deduction of (6) is given in 16 in Appendix A.…”

Section: ) Joint Probability Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MRD-Nets: Multi-Scale Residual Networks With Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal

Liu

Zhang

et al. 2019

IEEE Access

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“…In the process of recognition, the PCA feature method is used to reduce the dimension of the data, and the results of the classification are better. However, there are fewer sample set types, the amount of data is small, and the classification accuracy is not high [ 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

PLoS ONE

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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.

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“…However, with a smaller sample size for data analysis [6,7], the curse of dimensionality, and, concurrently, linear classification faults, this technology may well show its deficiencies [8,9]. At present, there are several more mature classification methods that can be applied to hyperspectral data, including support vector machines (SVMs) and spectral angle mapping (SAM) [10][11][12][13][14][15]. With SVMs [16,17], storage and computing are expensive when the number of training samples is large.…”

Section: Introductionmentioning

confidence: 99%

The Deep Belief and Self-Organizing Neural Network as a Semi-Supervised Classification Method for Hyperspectral Data

Lan

et al. 2017

Applied Sciences

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Hyperspectral data is not linearly separable, and it has a high characteristic dimension. This paper proposes a new algorithm that combines a deep belief network based on the Boltzmann machine with a self-organizing neural network. The primary features of the hyperspectral image are extracted with a deep belief network. The weights of the network are fine-tuned using the labeled sample. Feature vectors extracted by the deep belief network are classified by a self-organizing neural network. The method reduces the spectral dimension of the data while preserving the large amount of original information in the data. The method overcomes the long training time required when using self-organizing neural networks for clustering, as well as the training difficulties of Deep Belief Networks (DBN) when the labeled sample size is small, thereby improving the accuracy and robustness of the semi-supervised classification. Simulation results show that the structure of the network can achieve higher classification accuracy when the labeled sample is deficient.

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A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM

Cited by 10 publications

References 37 publications

MRD-Nets: Multi-Scale Residual Networks With Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal

MRD-Nets: Multi-Scale Residual Networks With Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal

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

The Deep Belief and Self-Organizing Neural Network as a Semi-Supervised Classification Method for Hyperspectral Data

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