Faulty measurement detection

All plant measurements are subject to a degree of uncertainty, some more than others. The measurement errors can be described as either “Random,” which are unavoidable and normally small, or “Gross Errors,” produced often from faulty measuring equipment and inherently avoidable. “Gross Errors” tend to lead to large imbalances in material conservation equations.

The algorithms used in traditional data reconciliation systems are based on the assumption that no gross errors exist in the measured data. They therefore tend not to yield useful results when the input data contains such gross errors, which forces production accountants to have to tweak data and rerun the algorithm, possibly many times. AVEVA Production Accounting distinguishes itself by being able to detect gross errors and treat them appropriately, excluding them from the balance, so that useful results are often generated on the first run.

The task of data reconciliation is to find an optimal solution that satisfies the model conservation equations. However, if gross errors do exist, as is often the case, traditional reconciliation solutions may be highly biased. Therefore the reconciled values for some measured variables may be worse than their corresponding raw values. Consequently, it is imperative to identify all gross errors in the measurements and eliminate them before final data reconciliation is obtained. The distinguishing feature of AVEVA Production Accounting is that it helps to identify those gross errors and does the elimination automatically using criteria that you specify.

Example

In the above example, the red colored measurements are detected as the faulty measurements. If we run data reconciliation without the faulty measurement detection step, the reconciled values of most flows show a big difference from measured values.

But if we run data reconciliation with a faulty measurement detection step, the three red measurements are detected as faulty measurements and the algorithm can eliminate these from the balance, treating them as unmeasured. Therefore the reconciled values are close to the measured values, except in the case of the three faulty measurements (as would be expected).

How AVEVA Production Accounting handles gross errors due to faulty measurements

AVEVA Production Accounting has a proprietary algorithm to detect and eliminate gross errors due to faulty measurements. The algorithm is based on statistical tests and provides a general framework for detecting any type of faulty measurements that can be modeled.

A serial search scheme is used first to isolate the candidate sources of faulty measurements and then simultaneous identification/elimination of faulty measurements is accomplished.

The method for elimination of faulty measurements is to convert the faulty measurements into unmeasured flows and make a new reduced balance set in the absence of these faulty measurements.

Generally speaking, the faulty measurement detection step identifies gross errors and removes the faulty measurements from the set of redundant measured flows.

The performance and accuracy of the detection are well proven from very many applications in different real plants.