This study aims to develop comprehensive maintenance strategies tailored to enhance the dependability, performance, and lifespan of critical assets within industrial and organizational settings. By integrating proactive, preventive, predictive, and corrective maintenance tactics, our strategy seeks to minimize downtime, reduce costs, and optimize asset performance. Drawing from extensive case studies across various industrial sectors, our research utilizes robust data analysis to inform strategy development.

Health prognosis is an advanced approach for anticipating the future status of systems, structures, and components. While it is accepted as an important step in boosting maintenance performance and resilience of a system, the subject of post-prognosis maintenance decision-making remains unsettled. To address this problem, we present one of the most effective economic criteria for concurrently assessing the performance and resilience of the time-based and condition-based maintenance methods. This criteria is a linear combination of the asymptotic average cost per unit of time and the standard deviation of the mean cost per renewal cycle of maintenance charges per renewal cycle. Ultimately, we will evaluate these two maintenance procedures to select the one that gives the optimum mix of lifetime and robustness for our system. We will also study how to fine-tune our new criteria to obtain the ideal balance of performance and robustness for two systems, the first is a system with changeable behavior, while the second one presents a system with more or less stable behavior. The inclusion of the Monte Carlo method improves the comparative study of maintenance methods, delivering insights into the performance and resilience of each adaptation in decision-making.

This essay analyzes the crucial role of maintenance in production and service-oriented enterprises, highlighting the necessity for integrated viewpoints in contemporary corporate management. Focusing on time-based maintenance (TBM) and condition-based maintenance (CBM) techniques, the study provides a novel criteria and applies Monte Carlo simulation for assessment. The purpose is to discover ideal values for the system downtime cost rate, establishing a balance between performance and resilience. The organized parts include deterioration and failure models, maintenance assumptions, and cost models, finishing with a summary of noteworthy results and recommendations for further investigations. The study brings significant insights into the subject of maintenance strategies and analyzes the influence of the system downtime cost rate on the performance and robustness of condition-based maintenance strategies for a system with more or less stable behavior.

This study provides a comprehensive evaluation of maintenance strategies for production systems subjected to stochastic degradation, with a comparative analysis of both traditional and advanced approaches. Traditional models, such as block replacement (BR) and quantile-based inspection and replacement (QIR), are critically examined in terms of their cost-effectiveness and robustness. While BR relies on fixed replacement intervals, QIR dynamically adjusts inspection schedules based on system degradation profiles, aiming to optimize maintenance expenditures and enhance system availability. However, as industrial systems grow increasingly complex, there is a clear need for more advanced maintenance strategies that offer superior performance and adaptability. In this context, the study explores advanced strategies, including the proportional hazards model (PHM) for condition-based maintenance (CBM), reinforcement learning-based maintenance (RL-M), and hybrid predictive–preventive maintenance (PPM). PHM-CBM leverages real-time degradation data for dynamic and optimal scheduling, RL-M utilizes machine learning algorithms to iteratively refine maintenance decisions, and Hybrid PPM integrates predictive analytics with preventive actions to ensure consistent cost control and system reliability. To facilitate a rigorous evaluation, this study proposes a novel cost criterion that integrates long-term cost rate projections with the variability observed across renewal cycles, providing a balanced assessment of both performance and robustness. The analysis is conducted using Monte Carlo simulations and stochastic renewal theory, offering a benchmark for comparing the various maintenance strategies. Ultimately, the study underscores the importance of further quantitative comparisons between advanced and traditional maintenance policies.