6.0. Human Reliability Analysis in Brief
From various internationally published estimates, it is found that in the world, out of the total number of plant accidental scenarios, around 60–90% are on account of human failure in different forms, and the rest are on account of technical deficiencies of equipment and control systems, or on account of other issues. Naturally, human reliability assessment or analysis claims is a major focus. In Fig. V/6.0-1A, a short depiction (Ref: Clause 6.4 also) has been presented to demonstrate understanding of how does it happens.
In almost all the PHA methods discussed so far, human error is considered as part of it, for example, human error and other systemic errors during operations, maintenance, testing, and restorations should be considered in assigning the PFD for IPL. For the manufacturing process, human errors have direct impact on the product, and this may increase rejection rate and reduce productivity, sales, and company reputation. The basic objective of an HRA is to evaluate the reliability of an operator’s action, and find the contribution of it in total reliability of the system. In doing so, HRA also evaluates the following:
Figure V/6.0-1 Human reliability analysis fundamentals. (A) Human action and accidental scenario, (B) HRA method.
• Predict human error rates.
• The degradation to human–machine interface systems due to human errors.
• Other human characteristics influencing the system behavior.
There have been several methods used to assess human reliability. Out of various methods, the technique for human error rate prediction (THERP) is in use since the beginning and still quite popular. Many of the HRA methods have been developed for specifically for various plants, for example, nuclear action reliability assessment (NARA). Short-working methods of important HRA methods, used as general purpose, in majority plants are shown in Fig. V/6.0-1B.
6.1. Short Human Reliability Analysis Steps
• Problem definition: Define the issue in the question.
• Task analysis: Description of task and understanding of the system by analyst.
• Error detailing: Listing of all probable errors and classification of various kinds of errors to identify various failure criteria.
• Development: For analysis preparation; development of risk tree and/or models.
• Qualitative analysis.
• Quantitative analysis:
• Impact assessment: Impact of error on the system.
• Total risk contribution from each action.
• Error reduction methods, for example, redesign, training.
• Quality assurance using suitable techniques.
• Documentation.
6.2. Brief Description of Commonly Used Human Reliability Analysis Methods
6.2.1. Technique for Human Error Rate Prediction (THERP)
The following are major steps to be followed:
• Familiarization: System information analysis.
• Qualitative analysis by task analysis, and development of human event tree.
• Quantitative Analysis: Sub-steps:
• Assignment of nominal human error probability (HEP).
• Impact/relative effect on performance shaping factor.
• Assessment of dependence.
• Assessment of success/failure probabilities.
• Determination of recovery factor.
• Sensitivity analysis.
• Documentation.
Note that there are standard guidelines available for HEP (such HSE UK or Norwegian Oil and Industry Association (OLF) provide such guidance for offshore/onshore explorations) and a dependence actor.
6.2.2. Success Likelihood Index Method
The purpose of this section is to assess HEP, and following steps may be performed:
• Defining the “actions.”
• Performance shaping factor (PSF) rating (scale of 0-10) and weightage (lo, med, hi): PSF is influenced by work force deployment, safety culture, behavioral safety, work design (shift change, work permit etc.), training and experience, procedural guidance, time adequacy, human–machine interface, task complexity, and stress factor.
• Classifying the actions.
• Calibrating success likelihood index (SLI) as per defined formula.
• Transforming SLI into HEP as per standard formula in logarithmic scale.
6.2.3. ATHENA
Following are the steps followed for analysis by ANTHENA:
• Definition of scope, then definition and understanding of the issue.
• Describe probabilistic risk assessment accident scenario.
• Definition of associated human failure event (HFE).
• Assessment of human performance with associated information.
• Identification of deviations of the PRA scenario.
• To identify potential recovery factor.
• Estimation of HEPs for HFEs and apply to PRA.
6.3. Variations in Human Reliability Analysis
There have been a variety of human reliability analyses/assessments. All of them have been evolved based on their usage and applications in various types of plants. A few of them have been developed specifically for a particular plant, while a few are in use as a general way.
• HRA category: A list of major HRA methods is available in HSE.UK-rr679 (2009). Out of so many HRA methods, few of them have been listed in the following clause. There are mainly three types of HRA methods, under which all of them can be categorized.
• 1st generation: HRA under this category are mainly concerned with the behavioral aspect of human, for example, THERP.
• 2nd generation: HRA under this category mainly deals with the conceptual side or on cognitive aspect of human, for example, ANTHENA.
• Expert judgment: There is another category of HRA which utilizes expert judgment, for example, SLIM-MAUD.
6.4. Human Reliability Analysis Types, Principles and Usages
A few important HRA types are listed in the following section.
6.4.1. 1st Generation: Behavioral Type
• ASEP: Accident sequence evaluation program. It is almost THERP (discussed in later), but requires fewer resources. It is mainly developed in the United States for nuclear program. It can be used by non-HRA specialists. Both pre- and post-accident quantification of HFEs are done in ASEP. Usually, it gives conservative output. It is used for nuclear plants.
• SPAR-H: Standardized plant analysis risk human reliability analysis: It is useful for the cases where detailed assessments are not called for, as it does not identify or model HFEs. It is possible to quantify HEPs for pre-initiator and post-initiator HFEs. It was initially used for nuclear application, but now has wider applications [12].
• SLIM: Success likelihood index method. It is an HRA quantification technique by which HEPs are quantified. For taking actions, this may be utilized in conjunction with multi-attribute utility decomposition (MAUD), discussed later. Here, SLI (Ref: Clause 6.2.2) is calibrated. It should actually be considered under expert judgment type. It has wider application as it is somewhat generic.
• HCR: Human cognitive reliability (operator reliability experiments, ORE). These are actually developed to quantify post-initiator human actions (e.g., actions performed by control room crews associated with emergency and abnormal operating procedures). These were meant for nuclear applications.
6.4.2. 2nd Generation: Cognitive Aspect
• ATHENA: A technique for human error analysis. ATHENA is one HRA method which has been developed to improve the state-of-the-art in HRA, especially with respect to how realistically HRA can represent the kinds of human behaviors seen in accidents and near-miss events. It requires a good amount of resources. ATHENA approach incorporates the current understanding of why errors occur, based on the work of earlier pioneers, and substantiated by reviews of a number of significant accidents. It has been developed for mainly for nuclear application, but now it is used in generic manner.
• CREAM: Cognitive reliability and error analysis method. In CREAM, the operator model is more significant and less simplistic than that of first generation approaches. It can be used both for performance prediction as well as accident analysis. CREAM is used for evaluation of the probability of a human error for completion of a specific task. There is good application of fuzzy logic in this method. It was again started for nuclear application but has wider applications, too.
6.4.3. Expert Judgment
SLIM-MAUD: SLIM (as well as FLIM) method requires expert judgment and when they are used with an interactive computer program called multi-attribute utility decomposition (MAUD). It is called SLIM-MAUD.
6.4.4. Current Generation
There have been further developments in NARA meant for nuclear applications. This type removes the limitation of 2nd generation HRAs. Now, an advanced version of HEART is used for the nuclear field. Also, there are a few other types such as justified human error data information, cognitive environmental simulation, etc. but these are not yet publicly available [12].
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