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.

Transformers are one of the most important part in a power system and, especially in key-facilities, they should be closely and continuously monitored. In this context, methods based on the dissolved gas ratios allow to associate values of gas concentrations with the occurrence of some faults, such as partial discharges and thermal faults. So, an accurate prediction of oil-dissolved gas concentrations is a valuable tool to monitor the transformer condition and to develop a fault diagnosis system. This study proposes a nonlinear autoregressive neural network model coupled with the discrete wavelet transform for predicting transformer oil-dissolved gas concentrations. The data fitting and accurate prediction ability of the proposed model is evaluated in a real world example, showing better results in relation to current prediction models and common time series techniques.

Purpose: Maintenance management is perceived as fundamental for ensuring that physical assets contribute to business outcomes. In this context, the purpose of this paper is to elaborate a novel maintenance management framework for asset management (MMFAM) based on ISO 55000 series guidelines.Design/methodology/approach: The paper proposes an MMFAM that intends to translate the technical requirements set out in the ISO 550001 standard to the maintenance stage of asset management. First, the framework is modeled using consideration based on the analysis of the ISO 55000 series and the review of previous maintenance management frameworks. Then, support tools are presented that collaborate for the implementation of the processes and activities provided for in the MMFAM.Findings: The proposed MMFAM was modeled using a Business Process Model and Notation since it is a standardized graphical notation for process modeling and contributes to the understanding of the framework. In addition, the applicable tools were presented for the practical implementation of MMFAM processes and activities.Practical implications: The paper is expected to contribute to maintenance practitioners and researchers in understanding and disseminating a novel maintenance management framework that is in line with asset management through the international ISO 55000 series.Originality/value: As it is essentially multidisciplinary and complex, asset management has still been little explored. Moreover, the requirements for implementing an asset management system, as explained in the ISO 55001 standard, only provide what needs to be done, but not how to do it. Accordingly, the paper fills a gap in maintenance management literature, as it addresses the ISO 55000 series for asset management in depth.

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.