College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2019 Oct 29;19(21):4702. doi: 10.3390/s19214702.
For a spacecraft, its power system is vital to its normal operation and capacity to complete flight missions. The storage battery is an essential component of a power system. As a spacecraft spends more time in orbit and its storage battery undergoes charge/discharge cycles, the performance of its storage battery will gradually decline, resulting in abnormal multivariate correlations between the various parameters of the storage battery system. When these anomalies reach a certain level, battery failure will occur. Therefore, the detection of spacecraft storage battery anomalies in a timely and accurate fashion is of great importance to the in-orbit operation, maintenance and management of a spacecraft. Thus, in this study, based on storage battery-related telemetry parameter data (including charge/discharge currents, voltages, temperatures and times) downloaded from an in-orbit satellite, a voltage anomaly detection algorithm for spacecraft storage batteries based on a deep belief network (DBN) is proposed. By establishing a neural network (NN) model depicting the correlations between each of the variables of temperature, current, pressure and charge/discharge times and voltage, this algorithm supports the detection of anomalies in the state-of-health of a storage battery in a timely fashion. The proposed algorithm is subsequently applied to the storage battery of the aforementioned in-orbit satellite. The results show the following. The anomalies detected using the proposed algorithm are more reliable, effective and visual than those obtained using the conventional multivariate anomaly detection algorithms. Compared to the classic backpropagation NN-based algorithm, the DBN-based algorithm is notably advantageous in terms of the model training time and convergence.
对于航天器来说，其电源系统对于其正常运行和完成飞行任务的能力至关重要。储能电池是电源系统的重要组成部分。随着航天器在轨道上停留的时间越来越长，其储能电池经历充放电循环，其性能将逐渐下降，导致储能电池系统的各种参数之间出现异常的多元相关性。当这些异常达到一定水平时，电池将出现故障。因此，及时准确地检测航天器储能电池的异常情况对于航天器的在轨运行、维护和管理非常重要。因此，在这项研究中，基于从一颗在轨卫星下载的与储能电池相关的遥测参数数据（包括充放电电流、电压、温度和时间），提出了一种基于深度置信网络（DBN）的航天器储能电池电压异常检测算法。该算法通过建立一个神经网络（NN）模型来描述温度、电流、压力和充放电时间与电压之间的各个变量之间的相关性，支持及时检测储能电池健康状况的异常。随后将该算法应用于上述在轨卫星的储能电池。结果表明：与传统的多元异常检测算法相比，该算法检测到的异常更可靠、更有效、更直观；与基于经典反向传播神经网络的算法相比，基于 DBN 的算法在模型训练时间和收敛性方面具有明显优势。
