Current Human Reliability Analysis Methods Applied to Computerized Procedures

Computerized procedures (CPs) are an emerging technology within nuclear power plant control rooms. While CPs have been implemented internationally in advanced control rooms, to date no US nuclear power plant has implemented CPs in its main control room (Fink et al., 2009). Yet, CPs are a reality of new plant builds and are an area of considerable interest to existing plants, which see advantages in terms of enhanced ease of use and easier records management by omitting the need for updating hardcopy procedures. The overall intent of this paper is to provide a characterization of human reliability analysis (HRA) issues for computerized procedures. It is beyond the scope of this document to propose a new HRA approach or to recommend specific methods or refinements to those methods. Rather, this paper serves as a review of current HRA as it may be used for the analysis and review of computerized procedures

Crosswalk of Human Reliability Methods for Offshore Oil Incidents

PresentationHuman reliability analysis (HRA) has long been employed in nuclear power applications to account for the human contribution to safety. HRA is used qualitatively to identify and model sources of human error and quantitatively to calculate the human error probabilities of particular tasks. The nuclear power emphasis of HRA has helped ensure safe practices and risk-informed decision making in the international nuclear industry. This emphasis has also tended to result in a methodological focus on control room operations that are very specific to nuclear power, thereby potentially limiting the applicability of the methods for other safety critical domains. In recent years, there has been interest to explore HRA in other domains, including aerospace, defense, transportation, mining, and oil and gas. Following several high profile events in the oil and gas industry, notably the Macondo well kick event in the U.S., there has been a move to use HRA to model and reduce risk in future oil drilling and production activities. Organizations like the Bureau of Safety and Environmental Enforcement are adapting the risk framework of the U.S. Nuclear Regulatory Commission for offshore purposes. In this paper, we present recent work to apply HRA methods to the analysis of offshore activities. We present the results of retrospective analyses using three popular HRA methods: SPAR-H, Petro-HRA, and CREAM. With the exception of Petro- HRA, these HRA methods were developed primarily for nuclear power event analysis. We present a comparison of the findings of these methods and a discussion of lessons learned in applying the methods to offshore events. The objective of this paper is to demonstrate the suitability of HRA methods for oil and gas risk analysis but also to identify topics where future research would be warranted to tailor these HRA methods

Retrospective Application of Human Reliability Analysis for Oil and Gas Incidents: A Case Study Using the Petro-HRA Method

Human reliability analysis (HRA) may be performed prospectively for a newly designed system or retrospectively for an as-built system, typically in response to a safety incident. The SPAR-H HRA method was originally developed for retrospective analysis in the U.S. nuclear industry. As HRA has found homes in new safety critical areas, HRA methods developed predominantly for nuclear power applications are being used in novel ways. The Petro-HRA method represents a significant adaptation of the SPAR-H method for petroleum applications. Current guidance on Petro-HRA considers only prospective applications of the method, such as for review of new systems to be installed at offshore installations. In this paper, we review retrospective applications of Petro-HRA and analyze the Macando Oil Well-Deepwater Horizon accident as a case study

The Measure of Human Error: Direct and Indirect Performance Shaping Factors

The goal of performance shaping factors (PSFs) is to provide measures to account for human performance. PSFs fall into two categories—direct and indirect measures of human performance. While some PSFs such as “time to complete a task” are directly measurable, other PSFs, such as “fitness for duty,” can only be measured indirectly through other measures and PSFs, such as through fatigue measures. This paper explores the role of direct and indirect measures in human reliability analysis (HRA) and the implications that measurement theory has on analyses and applications using PSFs. The paper concludes with suggestions for maximizing the reliability and validity of PSFs

COMMERCIAL UTILITY PERSPECTIVES ON NUCLEAR POWER PLANT CONTROL ROOM MODERNIZATION

Commercial nuclear power plants (NPPs) in the United States need to modernize their main control rooms (MCR). Many NPPs have done partial upgrades with some success and with some challenges. The Department of Energy’s (DOE) Light Water Reactor Sustainability (LWRS) Program, and in particular the Advanced Instrumentation and Controls (I&C) and Information Systems Technologies Research and Development (R&D) Pathway within LWRS, is designed to assist commercial nuclear power industry with their MCR modernization efforts. As part of this framework, a survey was issued to utility representatives of the LWRS Program Advanced Instrumentation, Information, and Control Systems/Technologies (II&C) Utility Working Group to obtain their views on a range of issues related to MCR modernization, including: drivers, barriers, and technology options, and the effects these aspects will have on concepts of operations, modernization strategies, and staffing. This paper summarizes the key survey results and discusses their implications

SPAR-H Step-by-Step Guidance

Step-by-step guidance was developed recently at Idaho National Laboratory for the US Nuclear Regulatory Commission on the use of the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method for quantifying Human Failure Events (HFEs). This work was done to address SPAR-H user needs, specifically requests for additional guidance on the proper application of various aspects of the methodology. This paper overviews the steps of the SPAR-H analysis process and highlights some of the most important insights gained during the development of the step-by-step directions. This supplemental guidance for analysts is applicable when plant-specific information is available, and goes beyond the general guidance provided in existing SPAR-H documentation. The steps highlighted in this paper are: Step-1, Categorizing the HFE as Diagnosis and/or Action; Step-2, Rate the Performance Shaping Factors; Step-3, Calculate PSF-Modified HEP; Step-4, Accounting for Dependence, and; Step-5, Minimum Value Cutoff

PROOF OF CONCEPT FOR A HUMAN RELIABILITY ANALYSIS METHOD FOR HEURISTIC USABILITY EVALUATION OF SOFTWARE

An ongoing issue within human-computer interaction (HCI) is the need for simplified or “discount” methods. The current economic slowdown has necessitated innovative methods that are results driven and cost effective. The myriad methods of design and usability are currently being cost-justified, and new techniques are actively being explored that meet current budgets and needs. Recent efforts in human reliability analysis (HRA) are highlighted by the ten-year development of the Standardized Plant Analysis Risk HRA (SPAR-H) method. The SPAR-H method has been used primarily for determining humancentered risk at nuclear power plants. The SPAR-H method, however, shares task analysis underpinnings with HCI. Despite this methodological overlap, there is currently no HRA approach deployed in heuristic usability evaluation. This paper presents an extension of the existing SPAR-H method to be used as part of heuristic usability evaluation in HCI

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems

Extracting and Converting Quantitative Data into Human Error Probabilities

This paper discusses a proposed method using a combination of advanced statistical approaches (e.g., meta-analysis, regression, structural equation modeling) that will not only convert different empirical results into a common metric for scaling individual PSFs effects, but will also examine the complex interrelationships among PSFs. Furthermore, the paper discusses how the derived statistical estimates (i.e., effect sizes) can be mapped onto a HRA method (e.g. SPAR-H) to generate HEPs that can then be use in probabilistic risk assessment (PRA). The paper concludes with a discussion of the benefits of using academic literature in assisting HRA analysts in generating sound HEPs and HRA developers in validating current HRA models and formulating new HRA models

Joint System Prognostics For Increased Efficiency And Risk Mitigation In Advanced Nuclear Reactor Instrumentation and Control

The science of prognostics is analogous to a doctor who, based on a set of symptoms and patient tests, assesses a probable cause, the risk to the patient, and a course of action for recovery. While traditional prognostics research has focused on the aspect of hydraulic and mechanical systems and associated failures, this project will take a joint view in focusing not only on the digital I&C aspect of reliability and risk, but also on the risks associated with the human element. Model development will not only include an approximation of the control system physical degradation but also on human performance degradation. Thus the goal of the prognostic system is to evaluate control room operation; to identify and potentially take action when performance degradation reduces plant efficiency, reliability or safety