主成分分析
键合图
冗余(工程)
故障检测与隔离
计算机科学
控制理论(社会学)
断层(地质)
算法
可靠性工程
工程类
人工智能
数学
执行机构
控制(管理)
组合数学
地震学
地质学
作者
Ming Yu,Jian Meng,Renlong Zhu,Wuhua Jiang,Qiang Shen
标识
DOI:10.1088/1361-6501/ac9708
摘要
Abstract This paper develops a sensor condition monitoring method integrating the model-based bond graph (BG) technique and data-driven principal component analysis (PCA) for the dissimilar redundant actuation system of more electric aircraft with uncertain parameters. The uncertain dissimilar redundant actuation system is modeled by BG in linear fractional transformation form. After that, the analytical redundancy relations containing the nominal part and the uncertain part can be derived, based on which the adaptive thresholds and the fault signature matrix (FSM) can be obtained for robust fault detection and fault isolation. To improve the fault isolation performance under the multiple faults condition, a new fault isolation method integrating FSM and improved PCA (IPCA) is developed, where the possible fault set generated from the FSM is further refined by the IPCA module with an improved reconstruction algorithm and cyclic PCA monitoring model to achieve a more efficient fault isolation result. The effectiveness of the proposed approach is validated by simulation investigations.
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