Performance shaping factors

Consider a task analysis for Do-ability. Can the operators do what we have asked them to do Tasks and facilities should be designed with knowledge of ergonomic considerations and performance shaping factors (PSF) so that operator reliability can be designed into the task. 

Human errors may be dependent on the specific accident sequence displayed in the event tree, and, for that reason, may be included in the event tree. This requires the human-factors specialist to consider the context of the error in terms of stress, operator training in response to the accident, di.tgnosiic paiierns, environmental, and other performance-shaping factors. 

Some of the performance-shaping factors (PSFs) affect a whole task or the whole procedure, whereas others affect certain types of errors, regardless of the tasks in which they occur. Still other PSFs have an overriding influence on the probability of all types of error in all conditions. 

THERP (NUREG/CR-1278), is used to estimate HEPs for a risk assessment. It provides error probabilities for generic tasks and describes the process used to modify these rates depending on the specific performance shaping factors (PSFs) involved in the task... 

A PSA analyst is usually interested in determining the probability of error for a task first case, no HRA ev ent tree is needed unless performance on that task is affected by other factors whose probabilities should be diagramed. A description of the ta.sk and knowledge of the performance-shaping factors are sufficient to determine the probability of a single human error. 

The analyst also records opportunities to recover errors, and various performance shaping factors (called performance-influencing factors in this book) which will subsequently be needed as part of the quantification process. 

Such a task description invites task analysis, which would lead naturally to human reliability analysis (HRA). Indeed, perhaps the earliest work in this field applied HRA techniques to construct fault trees for aircraft structural inspection (Lock and Strutt 1985). The HRA tradition lists task steps, such as expanded versions of the generic functions above, lists possible errors for each step, then compiles performance shaping factors for each error. Such an approach was tried early in the FAA s human factors initiative (Drury et al. 1990) but was ultimately seen as difficult to use because of the sheer number of possible errors and PSFs. It is occasionally revised, such as in the current FRANCIE project (Haney 1999), using a much expanded framework that incorporates inspection as one of a number of possible maintenance tasks. Other attempts have been made to apply some of the richer human error models (e.g.. Reason 1990 Hollnagel 1997 Rouse 1985) to inspection activities (La-toreUa and Drury 1992 Prabhu and Drury 1992 Latorella and Prabhu 2000) to inspection tasks. These have given a broader understanding of the possible errors but have not helped better define the PoD curve needed to ensure continuing airworthiness of the civil air fleet. 

SLIM-MAUD (Embrey 1984) implements a related approach in which expert ratings are used to estimate human error probabilities (HEPs) in various environments. The experts first rate a set of tasks in terms of performance-shaping factors (PSFs) that are present. Tasks with known HEPs are used as upper and lower anchor values. The experts also judge the importance of individual PSFs. A subjective likelihood index (SLI) is then calculated for each task in terms of the PSFs. A logarithmic relationship is assumed between the HEP and SLI, allowing calculation of the human error probability for task j (HEPj) from the subjective likelihood index assigned to task j (SLIj). More specifically ... 

Jiang Ying-jie, et al. 2011. Review of performance shaping factor analysis methods. China Safety Science Journal 7(1) 66-72. 

The values from [52] apply for optimal conditions. If conditions are not optimal, they are modified by multipliers >1. These are called performance shaping factors (PSF) and determined on the basis of an assessment of the impact of the circumstances for the action. 

A data-informed model of performance shaping factors and their interdependencies for use in human reliability analysis... 

Groth, K. and A. Mosleh (2009). A new data-based classification for performance shaping factors. Technical report, University of Maryland Center for Risk and Reliability. 

In order to consider these dependencies and their respective uncertainties, in this paper an approach based on a fuzzy Bayesian network wiU be looked at. By mean of a BBN approach one can capture the dependencies among performance shaping factor through the conditional probability tables. Section two presents some concepts related to Bayesian networks and fuzzy logic, while section three presents the approach adopted, drawing on the opinions of the experts. Section four looks at the case study of the installation of an optical monitoring system in an onshore well in a Petrobras brown field. In section five some results and analyses are discussed, while in section six the conclusions are presented. 

Tables 1 to 3 show the conditional probabilities for the nodes of the Bayesian network presented in Fig. 3. Note that level 0 represents an inadequate condition, while level 1 represents an adequate condition. It must be noticed that is in these tables that the dependence among the performance shaping factor will be approached, i.e., how, probabilistically, for example, skills and experience make influence in operator s capacity.

Insufficient criteria for choosing human Performance Shaping Factors (PSF s)... 

Stress is an additional factor included in aU models except for SPAR-H. Yet, the definition of this factor is not homogeneous across the models. In particular, the EPRI HRA Calculator defines stress as the culmination effect of other performance shaping factors, such as environmental factors. Interestingly, the Surry dependence model relates stress as whether the conditions of the red paths or the orange paths of the reactor safety function trees are met. 

This is a pretty special HRA method, highlighting time as a far most important performance shaping factor (Hannaman et al., 1984). Using the method, HEP is scanned with the help of one-dimensional continuous reliability-versus-time curve. The other influencing factors may be then taken into consideration in the phase of curve selection finm appropriate family of pre-defined suitable time correlated curves. 

In the first generation of HRA methods, human failure was seen and investigated as random phenomenon, with some distribution in time formed by performance shaping factors influence. In HRA second generation method/framework ATHEANA, treatment of human failure is different, as it is seen as cause based consequence of error forcing context actuation. Still, the plant specific experience can lead to the conclusion that some residual randomness should be kept in hiunan failure model, similarly to the case of (equipment) dependent errors and residual common cause failures. 

HMI specifications and applied solutions as Piping and Instrumentation Diagrams (P ID), data formats, types of data obtained, ergonomics of control room and organizational factors directly influencing on operator performance and should be incorporated into HRA analysis. Under consideration should also be taken the performance shaping factors (PSFs), such as stress, estabUshed procedures affecting human operator performance, etc. 

The success likelihood index method (SLIM) was selected to evaluate HEP with regard identified performance shaping factors (PSFs) (Hmnphreys 1988). A systematic approach has been apphed to identify PSFs that have direct impact on hmnan-operator performance relating to successfully accomplishment required tasks. Usefulness of SLIM method is illustrated on following example. 

Modeller (TM), a method of task decomposition and prediction of task deviations. The TM also identifies critical Performance Shaping Factors (PSFs) that influence task performance and provides input to the Fuzzy Probabihty Estimator (FPE) for the quantification of human errors. 

The specific case study aims at analyzing the startup of a gas turbine used to drive the compressor of the butane/propane refrigeration section of an LPG storage and treatment complex. For the start up of the turbine a very specific and detailed sequence of actions is performed. The completion of actions includes tasks performed by operators from the control room as well as by on-site operators. Performance Shaping Factors (PSFs) that influence operators reliability have been identified and their quality has been rated for the specific site according to expert judgment from safety experts and on site observations from the human factor experts of the project. 

The focus of VR tool for the Risk Assessment is the investigation and integration of Human Factors (HF) aspects and loss or delays on safety-critical tasks using the information and output data from VR experiments, that represent in a real way the accidental scenarios identified. Human Errors and Performance Shaping Factors (PSFs) resulting in accidental scenario can be investigated by means of post-experiments replays and reviews. 

PSFs Performance Shaping Factors TM Task Modeller... 

The EFC is defined as the situation that arises when particular combinations of performance shaping factors (PSFs) and plant conditions create an enviromnent in which unsafe actions (UA) are more likely to occur. The UA is a mode of human fadure that results in the Human Fadure Event (HFE) and, thus, is a specific inappropriate action taken (error of commission) or not taken when needed (error of omission), that results in a degraded plant condition. 

The flight crew model has been described earlier (Ale et al., 2008). The influence of management on flight crew performance and on flight crew performance shaping factors currently is only crudely modelled as is described in Lin et al. (2008)... 

A list of the possible error modes of the tasks and Performance Shaping Factors (PSF) affecting activities. 

Information cormected to task (performance shaping factors, etc) does not have a direct effect on the Sequence Flow or Message Flow of the Process, but provide useful information about what data may be required to perform the activity, what influences the activity and/or what the activity can produce. 

Screen 2 (Fig. 3) is used to visualize the Performance Shaping Factors (PSF) affecting the task, while Screen 3 is used to visualize the information abont the operators ( Job Roles , e.g. CR operator, CR assistant), tools (e.g. gas detector, spanner), plant areas (e.g. control room), and plant components (e.g. pressure valve) inserted for the task. This form is automatically filled by the software collecting the information fi om the nodes generated during the specification of the steps of the task. 

Figure 3. List of all the performance shaping factors associated with the case study.

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