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.

Maintenance plays a crucial role in the availability of an asset. In particular, when a company’s assets are decentralized, logistical aspects directly impact maintenance management and, consequently, productivity. In the energy generation sector, this scenario is common in enterprises and projects in which distributed energy resources (DERs), such as small hydroelectric power plants (SHPPs), are considered. Hence, the objective of this work is to propose an application of generalized stochastic Petri nets (GSPN) for the planning and optimization of the maintenance logistics of a DER enterprise with two SHPPs. In the presented case study, different scenarios are modeled considering logistical aspects related to the availability of spare parts and the sharing of maintenance teams between plants. From the financial return resulting from the estimated energy generation and the operating cost of each simulated scenario, the most profitable one can be estimated. The results demonstrate the ability of GSPNs to estimate the influence of the number of spare parts and maintenance teams on the availability of DERs, allowing the optimization of costs related to maintenance logistics.

Decision-making regarding maintenance planning has become increasingly critical. In view of the need for more assertive decisions, methods, and tools based on failure analysis, performance indicators, and risk analysis have obtained great visibility. One of these methods, the Variation and Mode Effect Analysis (VMEA), is a statistically based method that analyses the effect of different sources of variations on a system. One great advantage of VMEA is to facilitate the understanding of these variations and to highlight the system areas in which improvement efforts should be directed. However, like many knowledge-based methods, the inherent epistemic uncertainty can be propagated to its result, influencing following decisions. To minimize this issue, this work proposes the novel combination of VMEA with Paraconsistent Annotated Logic (PAL), a technique that withdraws the principle of noncontradiction, allowing better decision-making when contradictory opinions are present. To demonstrate the method applicability, a case study analyzing a hydrogenerator components is presented. Results show how the proposed method is capable of indicating which are the failure modes that most affect the analyzed system, as well as which variables must be monitored so that the symptoms related to each failure mode can be observed, helping in decision-making regarding maintenance planning.

The operation and maintenance of industrial plants constitute a major concern among managers, scientists, and engineers. To allow the prioritization of maintenance actions for improved continuous equipment operation, this paper presents a novel method focused on identifying the most critical components of a plant’s systems. In a complex engineering approach, this method uses tools and techniques borrowed from several fields of knowledge, including an innovative technique devised by the authors, as well as a multi-criteria decision method in the criticality determination process. To illustrate the method, a case study is presented considering a 150 MW Kaplan hydro generator from a hydroelectric power plant located in the north of Brazil. An uncertainty analysis is presented, considering variations in the scores of each criterion, to verify the robustness of the proposed method. The results show that the method responds in a solid and structured way, allowing decision making from qualitative and quantitative data and eliciting the opinion of experts on the factors that can affect the degradation of the performance of a system. In addition, the method’s robustness is confirmed since the uncertainty propagation related to the criteria scores is small for the items classified as most significant from its application.

Failure mode and symptoms analysis (FMSA) is a relatively new and still not very much employed variation of failure modes, effects and criticality analysis (FMECA), a technique broadly used in reliability, safety, and quality engineering. While FMECA is an extension of the well-known failure mode and effects analysis (FMEA) method, primarily used when a criticality analysis is required, FMSA focuses on the symptoms produced by each considered failure mode and the selection of the most appropriate detection and monitoring techniques and strategies, maximizing the confidence level in the diagnosis and prognosis. However, in the same way as FMECA and FMEA, FMSA inherits some deficiencies, presenting somewhat biased results and uncertainties intrinsic to its development, due to its own algorithm and the dependence on knowledge-based inputs from experts. Accordingly, this article presents a fuzzy logic application as a complement to FMSA in order to mitigate such uncertainties' effects. As a practical example, the method is applied to a Kaplan turbine shaft system. The monitoring priority number (MPN) obtained through FMSA is compared to the fuzzy monitoring priority number (FMPN) resulting from fuzzy logic application, demonstrating how the proposed method improves the evaluation of detection and monitoring techniques and strategies.