Identification of gross errors

Crowe, C.M., Recursive Identification of Gross Errors in Linear Data Reconciliation, AJChE Journal, 34(4), 1988,541-550. (Global chi square test, measurement test)... 

Madron, F, A New Approach to the Identification of Gross Errors in Chemical Engineering Measurements, Chemical Engineeiing Science, 40(10), 1985, 1855-1860. (Detection, elimination)... 

Madron, F. (1985). A new approach to the identification of gross errors in chemical engineering measurements. Chem. Eng. Sci. 40, 1855-1860. 

C. Aldrich and van J. S. J. Deventer Identification of gross errors in material balance measurements by... 

Statistically, a gross error is an error whose occurrence as realisation of a random variable is highly unlikely. It can arise out of inattention, a fault in the measuring instrument, erroneous calculation, or some other unforeseen event. The presence of a gross error corrupts also the results of other measurements and estimates, due to the propagation of errors in the reconciliation and unmeasured variables estimation. A detailed analysis lies beyond the scope of the present book. Let us only summarize the theoretical possibilities for detection and identification of gross errors. See further for instance Madron (1992). 

Theoretical possibilities for the detection and identification of gross errors are analyzed in Section 9.4. Assuming the measurement error vector... 

Crowe, C.M. (1988), Recursive identification of gross errors in linear data reconciliation, AIChE J. 34, 541-550... 

Let us recall the introducing paragraph to Section 9.4. From the statistical point of view, the detection and identification of gross errors based on nonlinear constraint equations is a delicate matter, even more than in the linear case. Some possibility is given when using the (pseudo)statistical characteristics of the solution (10.3.31) a. ff. 

How can the overall complex of data processing (reconciliation of redundant data, detection and identification of gross errors, etc.) be organized in an optimum way ... 

The presence of gross errors invalidates the statistical basis of the common data reconciliation procedures, so they must be identified and removed. Gross error detection has received considerable attention in the past 20 years. Statistical tests in combination with an identification strategy have been used for this purpose. A good survey of the available methodologies can be found in Mah (1990) and Crowe (1996). 

In Chapter 7 the problem of dealing with systematic gross biased errors is addressed. Systematic techniques are described for the identification of the source of gross errors and for their estimation. These techniques are computationally simple, they are well suited for on-line implementation, and they conform to the general process of variable monitoring in a chemical plant. 

This section briefly discusses an approach that combines statistical tests with simultaneous gross error identification and estimation. The strategy is called SEGE (Simultaneous Estimation of Gross Error Method). It was proposed by Sanchez and Romagnoli (1994). 

No. of gross errors correctly identified No. of gross errors simulated No. of gross errors wrongly identified No. of simulations trials No. of trials with perfect identification No. of simulations trials... 

For 12 of the 21 cases, a numerical value can be calculated for the overall performance of the SEGE strategy. For these combinations of gross errors, SEGE exhibits a larger fraction of perfect identification runs (OPF) compared to the other two methods. 

In this chapter we first presented a number of different, simple strategies for gross error identification. The serial elimination of measurements, the search along equations, and a combined procedure have been demonstrated to be simple and efficient ways for identifying gross errors. The estimation of gross errors due to both bias and... 

The simultaneous estimation of gross errors enhances identification performance and the accuracy of the estimation. This is a key characteristic when instruments cannot be repaired until the units are out of service. In these situations the corrected measurement data are used for control and optimization purposes. 

We have discussed, in Chapter 7, a number of auxiliary gross error detection/ identification/estimation schemes, for identifying and removing the gross errors from the measurements, such that the normality assumption holds. Another approach is to take into account the presence of gross errors from the beginning, using, for example,... 

Let us consider the process flowsheet that was presented in Fig. 3 of Chapter 7, which consists of a recycle system with four units and seven streams. In this case, two measurement errors are simulated in order to show the application of principal component strategy in their identification. Fixed gross error magnitudes of 5 and 7 standard deviations are considered for streams 1 and 2, respectively. 

The major question related to the rectification of gross errors is the identification of the sources. Quite often this question will be set aside and the gross error will be distributed among several measurements. Although this approach may suffice in the short run, it is unacceptable in the long run. The detection of a gross error requires the construction of an appropiate... 

The use of online data together with steady-state models, as in Real Time Optimization applications, requires the identification of steady-state regimes in a process and the detection of the presence of gross errors. In this paper a method is proposed which makes use of polynomial interpolation on time windows. The method is simple because the parameters in which it is based are easy to tune as they are rather intuitive. In order to assess the performance of the method, a comparison based on Monte-Carlo simulations was performed, comparing the proposed method to three methods extracted from literature, for different noise to signal ratios and autocorrelations. 

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