Since the beginning of human existence, humans have sought ways to reduce risks and threats in their living and working environments and to make them safer. Accidents such as airplane crashes, factory explosions, and similar events, which have resulted in extensive human and financial losses, have further emphasized the importance of accuracy and focus on methods for reducing the occurrence of such incidents. Moreover, with the rapid advancement of technology and the increasing complexity of systems, the severity of damages and consequences arising from accidents has increased. Therefore, enhancing the reliability level of equipment and systems, from simple to complex and even highly complex networks, is of great importance. The concept of reliability was first introduced during World War II in the context of improving the dependability of military equipment. The metal powder production industry is exposed to the risk of dust explosions due to dust generation. Therefore, analyzing the production system, identifying critical equipment, and evaluating the reliability of the system are among the most important ways to reduce the probability of explosions. In this paper, the fuzzy reliability of an aluminum powder production system is investigated based on statistical data, and it is demonstrated that fuzzy reliability is significantly more accurate than classical reliability.