## 3 Methodology
### Development Fuzzy Model  
The model for determining dependability will be presented through a hierarchical structure consisting of synthetic and partial indicators. In this sense, dependability (\(D\)) will be defined through partial indicators that are classified in the domain of reliability (\(R\)) and the domain of maintenance convenience (\(M\)) and logistical support (\(F\)), where the specified domains are synthetic indicators. Partial indicators of dependability are shown in Figure 3 and should include most of the phenomena and influential factors that lead to failure of the observed system. Figure 3: Presentation of partial indicators of dependability. The first step when creating a fuzzy model is the definition of linguistic variables that refer to the partial indicators of dependability, namely:  
Reliability represents the probability, at a certain level of confidence, that the system (machine) will successfully perform the function for which it is intended, without failure and within the specified performance limits, taking into account the previous time of system use, during the specified duration of a task. When it is used in the prescribed manner and for the purpose for which it is intended, under the specified load levels [11]. Maintainability, as a set of structural characteristics that affect the time to eliminate failures or the time of performing other maintenance procedures, is an internal property of the observed technical system; therefore, it is called structural maintainability. The following parameters affect the maintainability: t--technology, e--tools and equipment, u--unification, d--diagnostics, m--manipulativeness, s--standardization [11; 51]. For the technical system to successfully perform the set tasks, it is necessary to provide logistical support and numerous conditions. The logistic support combines the management process with appropriate technical measures to define the necessary support and create conditions for the realization of the given function of the technical system goal. Logistical support performances according to the ISO-IEC Standard are defined as: \"The ability of maintenance system, i.e., the organization that performs maintenance, to provide under given conditions the required maintenance of the technical system in accordance with the maintenance policy\" [48; 49], the Standards of the IEC 300 series deal with the concept of logistic support for maintenance [50]. In terms of the number of linguistic variables, it can be inferred that seven is the maximum count of variables that a person can rationally recognize simultaneously while retaining the same meaning [52]. Taking this statement into account, the five ratings (linguistic variables), defined as follows: excellent (exc), good (good), average (aver), adequate (adeq), and poor (poor), will be considered in this paper. Linguistic variables (ratings) are given in the form of triangular fuzzy numbers, and their graphic representation are presented in the following Figures 4 and 5. Corresponding fuzzy numbers of the mentioned linguistic variables are defined by (according to Figure 6):  
\[ \begin{array}{l}\mu_{poor}=(1,0.25,0,0,0),\\ \mu_{adeq}=(0.25,1,0.25,0,0),\\ \mu_{aver}=(0,0.25,1,0.25,0),\\ \mu_{good}=(0,0,0.25,1,0.25),\\ \mu_{exc}=(0,0,0,0.25,1).\end{array}  \tag{1}\]  
Figure 4: Fuzzy sets. Partial indicators \(t\), \(e\), \(u\), \(d\), \(m\), and \(s\) more closely determine the partial indicator \(M\)-maintenance convenience, while \(M\)-maintainability together with \(R\)-reliability and \(F\)  
Figure 5: Distribution of output values for indicator \(M\) maintainability for the CCS system, Crusher SB 1515 (**a**), Crusher SB 1315 (**b**), and belt conveyors (**c**) using max-min composition. Figure 6: Display results for parts of the CCS system. logistic support determine the \(D\)-dependability of the system.