Use of Corrective Action Programs at Nuclear Plants for Knowledge Management

Due to the uncertainty of many of the factors that influence the performance of the humans in nuclear power plant maintenance activities, we propose using Bayesian networks to model this kind of system. In this study, several models are built from the information contained in the Condition Reports from the Corrective Action Program at a nuclear power plant. This first study, using actual nuclear power plant data, includes a method for data processing and highlights some potential uses of Bayesian networks for improving organizational effectiveness in the nuclear power industry. The tool described in this paper is designed to provide a systematic approach to assist in managing an organization's knowledge base and support improvements in organizational performance. This paper describes the utilization of cause codes recorded in the Corrective Action Program for determining their effect on consequential events. Almost every organization manages data in some way. Data is a major corporate resource; however it is frequently poorly documented. Descriptions of data and other resources are metadata, which are part of the corporate memory for the organization, and preserving corporate memory is one of the basic features of knowledge management. At present, many countries are experiencing a large percentage of the personnel at nuclear power plants reaching retirement age. As a result, recording the memories of these workers, including the meaning of data, is increasingly important. Preserving metadata is crucial for understanding data years after the data were created. Human error and organizational performance is of special interest in any industry, and the nuclear industry has developed methods for performing Human Reliability Analysis (HRA) to calculate the contribution of human error to accidents. There have been attempts to collect data to inform quantification in HRA, starting with the work done for the THERP methodology by Swain (1). These efforts have continued to the present time, with efforts like the US Nuclear Regulatory Commission's Human Event Repository and Analysis (HERA) system (2) and the UK's CORE Database (3). However, many experts in HRA have related the opinion that there should have been more effort on collecting human error data for the purpose of quantifying the probabilities of human error (4); however, there does exist a wealth of information in the Condition Reports (CRs), products of the Corrective Action Programs (CAPs), at most nuclear plants. The corrective action process includes formal mechanisms to report, capture, assess, and correct organizational failures or shortcomings. Typically the focus is placed on identifying root causes and implementing corrective actions to ensure organizational learning and improvement (5). In fact, in the nuclear industry, we asseverate that the CAPs are a source of this metadata. The information contained in the CRs at every nuclear power plant is invaluable, and while the reports for each event may be lengthy, there should be an efficient way to record, store and retrieve data and feedback continually. The statistics of the data can tell us much about the trends in failures, whether system or human failures; however, if the information is not codified to work for the intended database, the results may be inaccurate. For this reason, this paper describes the review and work done to extract benefit from the root cause analysis done on any abnormal occurrence at a nuclear plant, and