Readiness and reliability is a special attribute of rescue systems (army, police, fire service), where performance at the highest level is more important than economic efficiency. For this reason, special attention is given to the process of renewal of technical objects. In such systems, a preventive strategy is most often used. Though this is a safe model, it does not always take into account the specifics of the use of a technical object. Moreover, in some situations, it forces the end of life of a device that could still continue to operate as intended. The article analyzes precisely such technical objects, removed from operation after just 10 years of use. It was shown that such approach is not justified and that modern management strategies must be implemented also in relation to machinery and equipment operating in rescue systems. The most important achievements of the article are the use of reliability analysis methods in the systems where it is not common, and the indication of the benefits of such analysis. It has been shown that knowing the characteristics of reliability, you can consciously control each process and make decisions in this regard based on the technical condition of the facility and not on instructions. In the case under study, this would make it possible to undermine the decision to withdraw the analyzed objects from operation.

In response to the urgent need for sustainable energy, this study addresses a critical challenge in wind turbine optimization. It focuses on developing a nuanced preventive maintenance strategy to minimize costs and mitigate energy losses. Within this framework, our paper introduces a novel approach employing a Monte Carlo simulation to identify the optimal preventive maintenance frequency, striking a balance between cost efficiency and energy loss mitigation. The results show, that grouped maintenance approach, pinpointing an optimal frequency of 93 months. This strategic configuration minimizes costs to $9997 while concurrently maintaining an average energy loss of 32.014 MWh, resulting in a notable 4.29% increase in total energy production. Variability analysis reveals that increasing maintenance frequency reduces cost fluctuations, while energy loss remains relatively stable. These findings elucidate the interplay among preventive maintenance strategies, cost, and reliability in the realm of wind turbine performance optimization