Paper Info
Title
Chaotic Ensemble of Online Recurrent Extreme Learning Machine for Temperature Prediction of Control Moment Gyroscopes.
Abstract
Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity of thermal environment on orbit, the temperature signal of the CMG has strong intrinsic nonlinearity and chaotic characteristics. Therefore, it is crucial to study temperature prediction under the framework of chaos time series theory. There are also several other challenges including poor data quality, large individual differences and difficulty in processing streaming data. To overcome these issues, we propose a new method named Chaotic Ensemble of Online Recurrent Extreme Learning Machine (CE-ORELM) for temperature prediction of control moment gyroscopes. By means of the CE-ORELM model, this proposed method is capable of dynamic prediction of temperature. The performance of the method was tested by real temperature data acquired from actual CMGs. Experimental results show that this method has high prediction accuracy and strong adaptability to the on-orbital temperature data with sudden variations. These superiorities indicate that the proposed method can be used for temperature prediction of control moment gyroscopes.
Year
DOI
Venue
2020
10.3390/s20174786
SENSORS
Keywords
DocType
Volume
control moment gyroscope,temperature prediction,online-recurrent extreme learning machine
Journal
20
Issue
ISSN
Citations 
17
1424-8220
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Luhang Liu100.34
Qiang Zhang224221.66
Dazhong Wei300.34
Gang Li400.34
Wu Hao55037.39
Zhipeng Wang6207.49
Bao-Zhu Guo71178117.67
Jiyang Zhang800.34