This paper aims to enhance the reliability of self-validating multifunctional sensors.
An effective fault detection, isolation and data recovery (FDIR) strategy by using kernel principal component analysis (KPCA) coupled with gray bootstrap and fault reconstruction methods.
The proposed FDIR strategy is able to the address fault detection, isolation and data recovery problem of self-validating multifunctional sensors efficiently.
A KPCA-based model which can overcome the limitation of existing linear-based models is used to achieve the fault detection task. By using gray bootstrap method, the position of all faulty sensitive units can be calculated even under the multiple faults situation. A reconstruction-based contribution method is adopted to evaluate the amplitudes of the fault signals, and the fault-free output of the faulty sensitive units can be used to replace the fault output.
This work is supported by the Fundamental Research Funds for the Central Universities (Grant No. HIT. NSRIF. 2017014). The authors would like to thank the editors and the reviewers for their helpful comments.
Yang, J., Sun, Z. and Chen, Y. (2017), "An efficient FDIR strategy on nonlinear processes of self-validating multifunctional sensors", Sensor Review, Vol. 37 No. 3, pp. 223-236. https://doi.org/10.1108/SR-08-2016-0138Download as .RIS
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