Open AccessMulti-label spacecraft electrical signal classification method based on DBN and random forest
Author(s) -
Ké Li,
Ningmei Yu,
Pengfei Li,
Shimin Song,
Yalei Wu,
Li Yang,
Meng Liu
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0176614
Subject(s) - random forest , deep belief network , computer science , feature extraction , artificial intelligence , artificial neural network , pattern recognition (psychology) , spacecraft , signal (programming language) , feature (linguistics) , data mining , algorithm , engineering , linguistics , philosophy , programming language , aerospace engineering
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.