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With the chosen tiered approach to transformation of heterogeneous terrestrial measurements, the error component ∆* can be reduced to a minimum.
Attention is focused on the analysis and design of a full-order fault detection filter such that, for all admissible unknown inputs and incomplete measurements, the error between residual and fault is kept as small as possible.
The main purpose of this paper is to design a robust fault detection filter such that, for all unknown inputs, possible parameter uncertainties and incomplete measurements, the error between the residual signal and the fault signal is made as small as possible.
Although the pressure mapping system tends to underestimate Ppeak, differences on the mean of the 3, 5 and 10 highest Ppeak range within ± 10%, while for the majority of the measurements the error on the integral of the acting pressures (the acting force compared with the force measured by the load cell) ranges within ± 20%.
Enrichment was calculated relative to RpL32 and values were normalized to input measurements; the error is indicated by the standard error of mean (SEM).
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We did not average nonresponders into the composite measurements, and the error measurements shown represent cells with differing response capabilities.
The smaller the measurement error, the smaller the changes that can be detected beyond measurement error.
The measurement error, the critical measurement error, the medically acceptable measurement error, and the decision levels l of the controls are measured in σ0 units, where σ0 is the standard deviation of the measurement error of the reliability state.
The measurement error is the error due to the spectral noise.
We can define risk measures based on the partial moments of the measurement error with reference to the medically acceptable measurement error (see Partial moments in Appendix S1).
Then we calculated the measurement error using the standard error of the mean σ m = S.D of measurements no. of measurements, where the number of measurements was five, the measurement error is independent of sample size.
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