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Availability is one of the most used terms in maintainability engineering. This concept is used to denote: The quality of service of an engineering system, i.e., machines, weak points' analysis, asset management, as well as making decisions in the process of life cycle management. Availability is an overall indicator and contains partial indicators that are oriented towards reliability, maintenance, and logistical support. Availability presents a variable value and changes in time and space. Usually, availability is shown as the coefficient of time use of the machine. This approach is not good enough because it does not go into the structure of the availability itself and requires a high level of IT support in system monitoring. In this sense, this paper will use the fuzzy theory and the corresponding analytic hierarchy process (AHP) multi-criteria analysis to present a conceptual and mathematical model for the assessment of availability based on expert judgment. The model will be shown in the case study (on the example) of bulldozers working in the open-pit lignite mine.Energies 2019, 12, 4044 2 of 18 defines availability as a measure of the degree to which an item is in an operable state and can be committed at the start of a mission when the mission is called for at an unknown (random) point in time. Availability, as measured by the user, is a function of how often failures occur and corrective maintenance is required, how often preventative maintenance is performed, how quickly indicated failures can be isolated and repaired, how quickly preventive maintenance tasks can be performed, and how long logistics support delays contribute to downtime [4].Achieving a satisfactory level of the machine availability in the use phase depends largely on appropriate maintenance procedures, logistical support, and the provision of appropriate means of maintenance. A variety of activities are involved in the attempt to reduce active and inactive maintenance time. The mentioned times are related to the maintenance work itself, as well as to the appropriate technical, logistical, and administrative waiting times. This requires appropriate efforts in planning and creating a realistic maintenance concept, a critical analysis of maintenance plans (maintenance levels, identification of needs, goals), defining requirements for logistics support tools (people, training, manuals, test, and auxiliary instruments, spare parts, etc.).In the last decades of the 20th century, the concept of dependability management [5,6] was developed by the International Electro-Technical Commission (IEC) in order to provide an integrated approach to managing and ensuring: Safety management, availability, reliability, convenience of maintenance, and maintenance support system. This concept is also designated as an international standard IEC 300 [5], which, among other things, states that the performance of dependability includes availability as its measure [6]. Dependability is defined in [7] as "the ability to avoid service failures that are more freque...

This paper presents a model for dependability performance evaluation by fuzzy sets utilization. Basic dependability indicators (reliability, maintainability and maintenance support) are used for the analysis of technical systems' conditions from the aspects of design, construction, maintenance and logistics. These indicators as well as associated dependability expressions itself are described by linguistic variables, which are characterized by a membership function to the defined classes. The proposed model is primarily appropriate for introduction, analysis and synthesis of information related to quality of systems in operation. Such data are often available only as experts' judgment and estimations. A practical engineering example (mechanical system at bucket wheel excavator) has been presented to demonstrate the proposed dependability analysis and synthesis model.

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The main goal of this research was the development of an algorithm for the implementation of negative risk parameters in a synthesis model for a risk level assessment for a specific machine used in the mining industry. Fuzzy sets and fuzzy logic theory, in combination with statistical methods, were applied to analyze the time picture state of the observed machine. Fuzzy logic is presented through fuzzy proposition and a fuzzy composition module. Using these tools, the symmetric position of the fuzzy sets with regard to class was used, and the symmetric fuzzy inference approach was used in an outcome calculation. The main benefit of the proposed model is being able to use numerical and linguistic data in a risk assessment model. The proposed risk assessment model, using fuzzy logic conclusions and min–max composition, was used on a mobile crushing machine. The results indicated that the risk level of the mobile crushing machine was in the “high” category, which means that it is necessary to introduce maintenance policies based on this high risk. The proposed risk assessment model is useful for any engineering system.

Rotary excavators as the basic machines at the open pits of lignite operate in very difficult working conditions, where they are constantly expected to be highly productive, reliable, available and safe as the production carriers. Determining the availability as well as the duration and number of failures using the analytical methods allows to analyze the key influencing factors on their occurrence and values of these parameters and to determine the essential elements of system maintenance and management in order to optimize them.