期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 66, 期 3, 页码 543-558出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2016.2642659
关键词
Data recovery; fault detection and isolation; self-adaptive multiple-variable reconstruction; self-validating multifunctional sensors; sparse non-negative matrix factorization
资金
- Fundamental Research Funds for the Central Universities [HIT.NSRIF.2017014]
A novel fault detection, isolation, and data recovery (FDIR) approach for self-validating multifunctional sensors is presented in this paper. To improve the fault detection accuracy under multiple steady conditions for multifunctional sensors, a sparse non-negative matrix factorization (SNMF)-based model is employed to accomplish fault detection through a combination of newly proposed C-2 and squared prediction error (SPE) statistics. Furthermore, a self-adaptive multiple-variable reconstruction strategy (SMVR) is proposed to achieve high accuracy on multiple-fault isolation and data recovery for faulty sensitive units. The performance of the proposed approach is fully verified in a real experimental system for self-validating multifunctional sensors, and it is compared with those of other fault detection models, such as principal component analysis (PCA), non-negative matrix factorization (NMF), and fault isolation algorithms, such as PCA-based contribution plots and SNMFbased contribution plots. The experimental results demonstrate that the proposed approach provides an excellent solution to the FDIR of self-validating multifunctional sensors.
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