A parametric mixture-model for common-cause failure data [of nuclear power plants]

1998 ◽  
Vol 47 (1) ◽  
pp. 30-34 ◽  
Author(s):  
P.H. Kvam
Keyword(s):  
Mixture Model ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Common Cause ◽  
Common Cause Failure ◽  
Failure Data

Digital I&C Operating Experience in the US

2008 ◽  
Author(s):  
Bruce Geddes ◽  
Ray Torok
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Failure Modes ◽  
Operating Experience ◽  
Data Set ◽  
Common Cause ◽  
Hardware Failure ◽  
Common Cause Failure ◽  
Defensive Measures

The Electric Power Research Institute (EPRI) is conducting research in cooperation with the Nuclear Energy Institute (NEI) regarding Operating Experience of digital Instrumentation and Control (I&C) systems in US nuclear power plants. The primary objective of this work is to extract insights from US nuclear power plant Operating Experience (OE) reports that can be applied to improve Diversity and Defense in Depth (D3) evaluations and methods for protecting nuclear plants against I&C related Common Cause Failures (CCF) that could disable safety functions and thereby degrade plant safety. Between 1987 and 2007, over 500 OE events involving digital equipment in US nuclear power plants were reported through various channels. OE reports for 324 of these events were found in databases maintained by the Nuclear Regulatory Commission (NRC) and the Institute of Nuclear Power Operations (INPO). A database was prepared for capturing the characteristics of each of the 324 events in terms of when, where, how, and why the event occurred, what steps were taken to correct the deficiency that caused the event, and what defensive measures could have been employed to prevent recurrence of these events. The database also captures the plant system type, its safety classification, and whether or not the event involved a common cause failure. This work has revealed the following results and insights: - 82 of the 324 “digital” events did not actually involve a digital failure. Of these 82 non-digital events, 34 might have been prevented by making full use of digital system fault tolerance features. - 242 of the 324 events did involve failures in digital systems. The leading contributors to the 242 digital failures were hardware failure modes. Software change appears as a corrective action twice as often as it appears as an event root cause. This suggests that software features are being added to avoid recurrence of hardware failures, and that adequately designed software is a strong defensive measure against hardware failure modes, preventing them from propagating into system failures and ultimately plant events. 54 of the 242 digital failures involved a Common Cause Failure (CCF). - 13 of the 54 CCF events affected safety (1E) systems, and only 2 of those were due to Inadequate Software Design. This finding suggests that software related CCFs on 1E systems are no more prevalent than other CCF mechanisms for which adherence to various regulations and standards is considered to provide adequate protection against CCF. This research provides an extensive data set that is being used to investigate many different questions related to failure modes, causes, corrective actions, and other event attributes that can be compared and contrasted to reveal useful insights. Specific considerations in this study included comparison of 1E vs. non-1E systems, active vs. potential CCFs, and possible defensive measures to prevent these events. This paper documents the dominant attributes of the evaluated events and the associated insights that can be used to improve methods for protecting against digital I&C related CCFs, applying a test of reasonable assurance.

Effective Software Verification and Validation Approach for Nuclear Power Plant Digital Instrumentation and Control Systems

2010 ◽  
Author(s):  
Steve Yang ◽  
Jun Ding ◽  
Huifang Miao ◽  
Jianxiang Zheng
Keyword(s):  
Life Cycle ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Software Verification ◽  
Verification And Validation ◽  
Common Cause ◽  
Digital Instrumentation ◽  
And Control ◽  
Software Verification And Validation

All 1000 MW nuclear power plants currently in construction or projected to-be-built in China will use the digital instrumentation and control (I&C) systems. Safety and reliability are the ultimate concern for the digital I&C systems. To obtain high confidence in the safety of digital I&C systems, rigorous software verification and validation (V&V) life-cycle methodologies are necessary. The V&V life-cycle process ensures that the requirements of the system and software are correct, complete, and traceable; that the requirements at the end of each life-cycle phase fulfill the requirements imposed by the previous phase; and the final product meets the user-specified requirements. The V&V process is best illustrated via the so-called V-model. This paper describes the V-model in detail by some examples. Through the examples demonstration, it is shown that the process detailed in the V-model is consistent with the IEEE Std 1012-1998, which is endorsed by the US Regulatory Guide 1.168-2004. The examples show that the V-model process detailed in this paper provides an effective V&V approach for digital I&C systems used in nuclear power plants. Additionally, in order to obtain a qualitative mathematical description of the V-model, we study its topological structure in graph theory. This study confirms the rationality of the V-model. Finally, the V&V approach affording protection against common-cause failure from design deficiencies, and manufacturing errors is explored. We conclude that rigorous V&V activities using the V-model are creditable in reducing the risk of common-cause failures.

Common cause failure model updating for risk monitoring in nuclear power plants based on alpha factor model

2017 ◽  
Vol 231 (3) ◽  
pp. 209-220 ◽  
Author(s):  
Min Zhang ◽  
Zhijian Zhang ◽  
Ali Mosleh ◽  
Sijuan Chen
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Model Updating ◽  
Factor Model ◽  
Failure Model ◽  
Common Cause ◽  
Common Cause Failure ◽  
Risk Monitoring ◽  
Common Cause Failures ◽  
The Common

Common cause failure model updating (both qualitatively and quantitatively) is a key factor in risk monitoring for nuclear power plants when configuration changes (e.g. components become unavailable) occur among a redundant configuration. This research focuses on the common cause failure updating based on the alpha factor model method, which is commonly used in the living probabilistic safety assessment models for nuclear power plant risk monitoring. This article first discusses the common cause failure model updating in an ideal condition, which evaluates the common cause failure model parameters for the configurationally changed system in different ways, based on the causes of the detected failures. Then, two alternative updating processes are proposed considering the difficulty to identify failure causes immediately during plant operation: one is to update the common cause failure models with the assumption that the failures detected are independent failures and the other is to update the common cause failure models with the parameters as expectations of the values for all possible failure causes. Finally, a case study is given to illustrate the common cause failure updating process and to compare these two alternative processes. The results show that (1) common cause failures can be reevaluated automatically by the methods proposed in this article and (2) the second process is more conservative and reasonable but with more data requirements compared with the first approach. Considering limitations in accessibility of the data, the first strategy is suggested currently. More future work on data acquisition is demanded for better assessment of common cause failures during nuclear power plant risk monitoring.

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