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Maintenance policies are crucial for ensuring the reliability, safety, and longevity of a system, as well as reducing the risk of accidents. Preventive maintenance (PM) is an effective strategy to keep equipment and systems in good working order by fixing potential issues before they cause downtime or safety hazards. However, optimizing the time intervals between PM activities is essential for minimizing the overall maintenance cost. This paper proposes an innovative approach that considers the intervention level of maintenance activities as an independent variable of PM times. The approach provides greater flexibility in creating maintenance plans, as it considers practical aspects that may impact maintenance activities beyond the time interval between PMs. The proposed approach uses a reliability model that incorporates imperfect preventive maintenance and a variable improvement factor based on age reduction. The improvement factor of each preventive maintenance activity (PMA) is defined based on the intervention level of the activity itself, which is determined by the number of tasks performed, execution time, and the number of items replaced in the maintenance plan. The proposed maintenance strategy determines not only the optimal times for PMAs and the respective intervention level but also the optimal number of maintenance activities that minimize the total maintenance cost along a fixed and user-defined planning horizon. The effectiveness and precision of the approach have been demonstrated through a series of numerical examples and a comprehensive case study involving three heat exchangers situated within the hydroelectric power plant. [ABSTRACT FROM AUTHOR]

This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an industrial context to improve systems' safety and reliability. In contrast to supervised methods, the approach requires less manual data labeling and can find previously unknown patterns in data. The technique focuses on detecting and isolating possible measurement divergences and tracking their growth to signalize a fault's occurrence while individually evaluating each monitored variable to provide fault detection and prognosis. Additionally, the paper also provides an appropriate set of metrics to measure the accuracy of the models, which is a common disadvantage of unsupervised methods due to the lack of predefined answers during training. Computational results using the Commercial Modular Aero Propulsion System Simulation (CMAPSS) monitoring data show the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]