Proper maintenance planning is critical for maintenance management to contribute to increasing availability, ensuring quality requirements, and controlling the safety and environmental risks associated with physical assets. As supporting tools for developing maintenance strategies, Reliability-Centered Maintenance (RCM) and Risk-Based Maintenance (RBM) methods are currently used in several organizations. Nevertheless, these strategies are often approached separately although they are complementary. In this context, this paper proposes a novel method that effectively integrates RCM and RBM by adapting the traditional RCM method to incorporate risk management into maintenance planning decision-making to support maintenance management. The proposed Reliability and Risk Centered Maintenance (RRCM) method allows organizations to determine maintenance plans that ensure the reliability of the physical assets while considering and prioritizing the risks associated with their potential functional failures. The proposed method was demonstrated through a case study considering the operational context of a hydroelectric power plant. The results show the ability of RRCM to assist in the development and implementation of maintenance plans oriented to reliability, risk, and cost.

Determining the ideal size of maintenance staff is a daunting task, especially in the operation of large and complex mechanical systems such as thermal power plants. On the one hand, a significant investment in maintenance is necessary to maintain the availability of the system. On the other hand, it can significantly affect the profit of the plant. Several mathematical modeling techniques have been used in many different ways to predict and improve the availability and reliability of such systems. This work uses a modeling tool called generalized stochastic Petri net (GSPN) in a new way, aiming to determine the effect that the number of maintenance teams has on the availability and performance of a coal-fired power plant cooling tower. The results obtained through the model are confronted with a thermodynamic analysis of the cooling tower that shows the influence of this system’s performance on the efficiency of the power plant. Thus, it is possible to determine the optimal size of the repair team in order to maximize the plant’s performance with the least possible investment in maintenance personnel.