A model-driven dual-derivation framework for quantitative fault detection in satellite power system

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pengming Wang , Liansheng Liu , Yuchen Song , Zhidong Li , Datong Liu
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引用次数: 0

Abstract

Satellite power system (SPS) fault detection is of great significance to ensure the safety and stability of satellites. On-orbit SPS can divide 11 near-mutation operating conditions (OCs) in 4 types of sunlight regions. Combined with limited fault samples and high-dimensional, coupled, and noisy telemetry data, the accuracy of data- or knowledge-driven SPS fault detection is poor. Therefore, this work first comprehensively considers the mechanism model of SPS and the quantitative analysis results of corresponding faults, based on which an SPS fault behavior model is configured. By combining specific driving and parameter updating methods, strong support is provided for on-orbit SPS digital twin and fault detection. Then, a model-driven dual-derivation quantitative fault detection framework that combines accuracy and robustness is proposed. To be specific, an adaptive integral residual (AIR) algorithm for constructing SPS OCs is developed, which combines telemetry data with twin data to determine fault states and obtain fault information. Using the tree-structured Parzen estimator (TPE), iteratively adjust the model’s failure modes and parameters to obtain simulated data for the current fault. By comparing it with fault telemetry data, determine whether the current failure modes and parameters meet the requirements of quantitative fault detection. Finally, a semi-physical experimental platform was established, and experimental results confirmed the framework’s capability to accurately differentiate between different levels of faults. Specifically, the quantitative detection accuracy for typical faults reached 100%. Additionally, we designed seven accuracy and robustness indicators, all of which yielded optimal results when compared with common methods. Through experimental analysis of search space optimization methods, the universality of optimization methods has been demonstrated.
用于卫星电力系统定量故障检测的模型驱动双衍生框架
卫星动力系统(SPS)故障检测对确保卫星的安全和稳定具有重要意义。在轨 SPS 可将 11 种接近突变的工作条件(OC)划分为 4 类日照区域。结合有限的故障样本和高维、耦合、高噪声的遥测数据,数据或知识驱动的 SPS 故障检测精度较低。因此,本文首先综合考虑了 SPS 的机理模型和相应故障的定量分析结果,在此基础上配置了 SPS 故障行为模型。结合具体的驱动和参数更新方法,为在轨 SPS 数字孪生和故障检测提供有力支持。然后,提出了一种兼具准确性和鲁棒性的模型驱动双衍生定量故障检测框架。具体而言,开发了一种用于构建 SPS OC 的自适应积分残差(AIR)算法,该算法将遥测数据与孪生数据相结合,以确定故障状态并获取故障信息。利用树状结构的帕尔森估计器(TPE),迭代调整模型的故障模式和参数,以获得当前故障的模拟数据。通过与故障遥测数据进行比较,确定当前的故障模式和参数是否满足定量故障检测的要求。最后,建立了一个半物理实验平台,实验结果证实了该框架准确区分不同等级故障的能力。具体来说,典型故障的定量检测准确率达到了 100%。此外,我们还设计了七个准确性和鲁棒性指标,与普通方法相比,这些指标都能获得最佳结果。通过对搜索空间优化方法的实验分析,证明了优化方法的普遍性。
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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