Anomaly and error detection in computerized materials control & accountability databases
- and others
Unites States Department of Energy sites use computerized material control and accountability (MC&A) systems to manage the large amounts of data necessary to control and account for their nuclear materials. Theft or diversion of materials from these sites would likely result in anomalies in the data, and erroneous information greatly reduces the value of the information to its users. Therefore, it is essential that MC&A data be periodically assessed for anomalies or errors. At Los Alamos National Laboratory, we have been developing expert systems to provide efficient, cost-effective, automated error and anomaly detection. Automated anomaly detection can provide assurance of the integrity of data, reduce inventory frequency, enhance assurance of physical inventory, detect errors in databases, and gain a better perspective on overall facility operations. The Automated MC&A Database Assessment Project is aimed at improving anomaly and error detection in MC&A databases and increasing confidence in the data. We are working with data from the Los Alamos Plutonium Facility and the Material Accountability and Safeguards System, the Facility`s near-real-time computerized nuclear material accountability and safeguards system. This paper describes progress in customizing the expert systems to the needs of the users of the data and reports on our results.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 543618
- Report Number(s):
- LA-UR-97-2550; CONF-970744-; ON: DE97008986; TRN: 97:005684
- Resource Relation:
- Conference: 38. annual meeting of the Institute of Nuclear Materials management, Phoenix, AZ (United States), 20-24 Jul 1997; Other Information: PBD: 1997
- Country of Publication:
- United States
- Language:
- English
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