Yang Jing-Li, Chen Yin-Sheng, Zhang Li-Li, Sun Zhen
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150080, China.
College of Basic Science, Harbin University of Commerce, Harbin 150028, China.
Rev Sci Instrum. 2016 Jun;87(6):065004. doi: 10.1063/1.4954184.
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.
本文提出了一种用于自验证多功能传感器的新型故障检测、隔离与诊断（FDID）策略。基于稀疏非负矩阵分解的方法可通过使用平方预测误差（SPE）统计量有效检测故障，基于SPE统计量的变量贡献图有助于定位和隔离故障敏感单元。采用完备总体经验模态分解将故障信号分解为一系列固有模态函数（IMF）和一个残差。估计每个IMF和残差的样本熵（SampEn）加权能量值以表征故障信号的特征。引入多类支持向量机来识别故障模式，目的是诊断故障敏感单元的状态。将所提出策略的性能与其他故障检测策略（如主成分分析、独立成分分析）以及故障诊断策略（如经验模态分解与支持向量机相结合）进行比较。在所搭建的实际自验证多功能传感器实验系统中对所提出的策略进行了全面评估，实验结果表明所提出的策略为自验证多功能传感器的FDID研究课题提供了一个出色的解决方案。
