## 5 Discussion and Conclusions  
In order to develop a model for determining the dependability of complex technical systems in mining, a combination of several research methods was necessary, such as a statistical analysis method, a conventional method for calculating dependability, and methods based on the application of fuzzy logic. These methods do not limit the use of some others. The goal is for them to show a match with historical data in their results. The main goal of the research in this paper is to identify important parameters that affect the dependability of these technical systems and to synthesize these indicators and determine dependability by applying fuzzy logic. The initial basis of the model is represented by expert evaluations obtained by a survey that combines the evaluation of the dependability as a whole as well as the evaluation of individual parts of the system. Synthesis assessment allows the behavior of the production system to be described with a relevant assessment even when not all its elements are known. In this way, a relatively quick assessment is possible by applying the presented model in the function of production planning, maintenance system planning, and evaluation of exploitation effects, where the assessment depends on important indicators assessed by experts, for a complex technological system and in complex exploitation conditions. This is proven by the presented model verification by comparison with historical data. Once the model is set, it no longer requires a lengthy analysis of a large set of historical data. The model itself can be improved over time by taking into account additional indicators that turn out to have a significant impact. This model will improve the operation of the CCS system and indicate a possible reduction in maintenance costs and coal exploitation costs. The presented model contributes to the assessment, understanding, and optimization of the dependability of the CCS system in surface coal mines. This model was developed using the example of the Gacko open pit, but it is also applicable to other open pits and can be especially significant for open pits where very different mining equipment is used, which requires specific types of maintenance and reacts differently to external influences. Therefore, it can be expected that the presented model can be successfully applied in cases where the mining equipment is unified to a greater extent and in different geological or climatic conditions. The model presented in this paper can be applied in other areas of the industry where similar production systems are applied. This paper presents a model for evaluating the dependability of technical systems using fuzzy logic. It breaks down dependability into different indicators and combines them using the max-min composition method. Figure 11: The dependability of the continuous part of the CCS system in the period 2018–2023. Unlike conventional models that rely on IT monitoring systems, this approach incorporates expert assessments from individuals directly involved in machine operation and maintenance. Its simplicity and reliance on expert judgment make it easy to implement without extensive data collection. This model offers a fast way to assess system safety and provides valuable insights for enhancing specific indicators and overall dependability. By following the model's recommendations, companies can streamline maintenance activities, analyze workflows, pinpoint weaknesses, and optimize the lifecycles of machinery to lower operational expenses. Field experience confirms that the model accurately reflects the dependability of analyzed systems, considering factors such as system components, structure, age, working conditions, and organizational influences. When comparing the reliability data obtained through the fuzzy logic model with the actual field data collected over the period from 2018 to 2023, there is a strong correlation.