Using polynomial smoothing and data bounding for the detection of nuclear material diversions and losses

Abstract

Successive nuclear material balances form a time series of often autocorrelated observations. Significiant deviations from the underlying in-control process model or time series pattern, i.e., outliers, indicate an adverse process change or out-of-control situation relative to the model. This paper uses these ideas to demonstrate a method for detecting nuclear material losses earlier than currently used models and thus helps to alleviate a problem which is growing to world crisis proportions. The process control capabilities of these methods were successfully tested on two autocorrelated data sets of nuclear material balances with known removals. These algorithms should be of assistance to the nuclear engineer involved in process control. A. INTRODUCTIONIn Nuclear Materials Accounting the goal is to accurately discover material losses (e.g., leakage or theft) as early as possible to minimize the threat to life and property. Despite some progress, control against leakage, theft, or loss of radioactive material has continued to be a problem. International terrorist activity and the desire of renegade nations to become nuclear powers make the threat of theft very real. Only recently it was discovered that controls for weapon grade nuclear materials are grossly deficient in the former Soviet Union as evidenced by several instances of stolen plutonium and uranium being found in Germany.1 Possible scenarios regarding the theft of weapons grade material are frightening.

The Use of Robust Smoothers in Nuclear Material Safeguards

“…As has been shown previously, nuclear material loss situations may be recognized through the detection of statistical outliers. In general terms, an outlier is an inconsistent or nonrepresentative point in a data set that causes a difficulty or problem in a statistical analysis.…”

“…Statistical methods that are in current use today have been roundly criticized by numerous authors. 3,7,8 Material Balance Equation.…”

“…3,17 The summary in column 1 of Table 2 It is crucial to detect an outlier as early as possible within the protracted period of removals. Polynomial smoothing detected the very first observation (no.…”

Combined Survey Sampling Inference

“…However, only Savitsky and Golay's original article is included as a reference. Two modi cations that are very important for proper use of the technique were published later; Sebastian, Booth, and Hu (1995) gave those references and discussed their use. 2.…”

A Robust Smoothing Approach to Statistical Process Control

“…A common scenario in which the IID assumption does not hold is when the data is autocorrelated, i.e. high points tend to be followed by high points and low points tend to be followed by low points. ,, When the IID assumption does not hold, traditional control charts may be ineffective and inappropriate for monitoring and controlling product quality. , It is far more realistic, based on previous research, to assume that autocorrelations among the data exist. ,, It is the purpose of this paper to present a method for statistical process control that utilizes the fact that the data are autocorrelated. Thus, since the method would be based on a more accurate assumption, it should lead to superior results.…”