故障检测与隔离
希尔伯特-黄变换
计算机科学
残余物
主成分分析
统计的
模式识别(心理学)
矩阵分解
熵(时间箭头)
支持向量机
断层(地质)
人工智能
数据挖掘
算法
数学
统计
滤波器(信号处理)
量子力学
物理
地质学
计算机视觉
特征向量
地震学
执行机构
作者
Jingli Yang,Yinsheng Chen,Lili Zhang,Zhen Sun
摘要
A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.
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