## 2 Materials and Methods  
Modeling the type of error that can occur in a human-machine system is probably the most important aspect of assessing and eliminating the human contribution to the risk of an accident [5]. For modeling reliability, the performance of dynamic systems, (solving repetitive tasks), it is preferable to use the systems approach applied in classical reliability theory. For this case, [PERSON] and [PERSON] [25; 26] defined a reliability function of human performance:  
\[\mathrm{R_{h}(t)=exp\ (-\int_{0}^{t}\mathrm{er(t)dt})\ [-]} \tag{1}\]  
The variable er(t) appearing in Equation (1) represents the instantaneous value of the human error rate under the given conditions. In practice, a mean value related to an infinite time interval, the number of work operations performed, and the number of individuals is used. Such variable is referred to as HEP and can be simplistically expressed as the ratio of the number of observed errors NoEi (Number of Erroneously performed tasks of type i) and the number of opportunities (occasions) to make errors NoTi (Number of all Tasks of type i), Equation (2).  
\[\mathrm{HEP=(N_{0}.E_{i})/(N_{0}.T_{i})\ [-]} \tag{2}\]  
To determine the HEP, it is necessary to precisely define the cases and states of human activity in the analyzed system (i.e., to determine deviations from the prescribed scheme), which means that it is necessary to start from the knowledge of specific errors that a human can make when performing a given task. For operator actions where the actions are performed as independent, relation 3 applies. For actions performed as dependent, relation 4 applies.  
\[\mathrm{P(A)=\sum_{i=1}^{n}P(A_{i})\ [-]} \tag{3}\]  
\[\mathrm{P(A)=\prod_{i=1}^{n}P(A_{i})\ [-]} \tag{4}\]  
The resulting probability of human failure is then determined as the logical product of these actions, which represents the particular work step being analyzed.  
The classification of the individual error groups is as follows [13]:  
* Activity errors A;
* Control errors C;
* Information retrieval errors R;
* Tracking/checking errors T;
* Selection errors S;
* Planning errors P.  
Subsequently, for each activity (or partial steps of a given activity), it is possible to select plausible error types from the HEP database and from them further specifically relevant errors, i.e., errors that can be expected to occur in the human-machine/equipment relationship. For each potential error, the possible consequences must be estimated--theprobability of error occurrence (HEP) or the correction of the HEP to a realistic level of operational safety.  
The HEP database was developed by gathering data from various expert sources that provide either generic or statistical information from different types of process industries. Compared to the statistically \"average\" probability of a specific error occurring in a given operation, which is the mean value obtained from multiple expert sources, the actual probability of an error might be either higher or lower. For each potential error, its possible consequences are evaluated. The consequences are expressed as the probability of occurrence (HEP) of the given error:  
* Low (L)--The occurrence of the error is nearly unpredictable at the current security level.
* Medium (M)--The error has been detected in the past, but the current security level limits its recurrence.
* High (H)--The error has occurred multiple times (by different team members) or repeatedly by the same employee, and its occurrence must be expected at the current system security level.  
Figure 1 illustrates the human reliability assessment process as outlined in the IEEE 1082 Guide for Incorporating Human Reliability Analysis into Probabilistic Risk Assessments for Nuclear Power Generating Stations and Other Nuclear Facilities [27; 28].