Data validation and dynamic uncertainty estimation of self-validating sensor

A novel self-validating strategy using grey bootstrap method (GBM) is proposed for data validation and dynamic uncertainty estimation of self-validating sensor. The failure detection, isolation, and recovery (FDIR) of self-validating sensor based on GM(1,1) predictor can simultaneously detect and isolate fault and accomplish failure recovery with high accuracy and good timeliness. Furthermore, the proposed FDIR scheme has good effectiveness of discriminating between fault-free signals with sudden changes and undoubted faults. In dynamic measurement process, because of unknown prior information about probability density functions (PDFs) of uncertainty sources, the uncertainty cannot be estimated by Guide to the Expression of Uncertainty in Measurement (GUM). The GBM can evaluate the measurement uncertainty by poor information and small sample. Experiment results show that the GBM strategy provides a good solution to data validation and dynamic uncertainty estimation of self-validating sensor.