A proactive opportunistic maintenance decision model based on reliability in train systems

<p>This paper describes the optimisation of bridge maintenance strategy and cost, utilising a Structures Asset Valuation and Investment (SAVI) tool and considering whole life costs, traffic delays and carbon emissions arising from the bridge maintenance programme. Parametric studies were carried out with SAVI to compare maintenance costs and traffic delays for different maintenance strategies. Carbon emissions for each maintenance strategy were estimated using a popular carbon calculator tool and monetised, based on UK carbon valuations, before incorporating into the whole life costing of the bridge. The results demonstrate that a planned preventive maintenance strategy was the most cost effective and sustainable strategy with 7.7% lower discounted maintenance costs and 7% less carbon emission compared to an unplanned reactive maintenance strategy.</p>

Auxiliary systems play a critical role in the reliable and safe operation of power plants, yet they remain a major source of unplanned downtime, maintenance cost escalation, and operational risk. Traditional maintenance approaches applied to these systems are often time-based or reactive, providing limited insight into failure behavior and inefficient allocation of maintenance resources. This study proposes an integrated maintenance strategy optimization framework that combines Reliability-Centered Maintenance (RCM) principles with stochastic failure modeling to enhance the reliability and cost-effectiveness of power plant auxiliary systems. A quantitative, reliability-based case study approach is adopted, focusing on key auxiliary systems including feedwater pumps, cooling water systems, lubrication units, air compressors, and ash handling systems. RCM analysis is used to identify critical components, dominant failure modes, and failure consequences, while stochastic failure models are developed to estimate failure rates, reliability functions, and system-level reliability profiles. Optimized maintenance intervals and policies are derived by balancing reliability, availability, cost, and risk considerations. The results demonstrate that the integrated RCM stochastic approach significantly improves Mean Time Between Failures (MTBF), system availability, and maintenance cost efficiency compared to conventional maintenance strategies. The study contributes a practical, data-driven framework for maintenance optimization and provides valuable insights for power plant asset management, particularly in environments characterized by aging equipment and resource constraints.

This paper proposes a quantitative approach for selecting effective maintenance strategies for metro tunnels in order to reduce maintenance cost and ensure tunnel safety in the presence of structural degradation during the service life. A non-stationary gamma process is used to simulate the tunnel gradual degradation and to investigate the effects of different maintenance measures on the life cycle maintenance cost of metro tunnels in soft soil. Two commonly applied reinforcement methods, Aramid Fibre Reinforced Polymer and bonded steel plate reinforcement, are investigated in this study. The feasibility of the proposed methodology is demonstrated with maintenance strategies based on periodic and aperiodic inspection policies. Numerical results show that the optimisation of maintenance strategy can reduce the life cycle maintenance cost of metro tunnels in soft soil while ensuring sufficient level of structural safety, and the maintenance strategy with aperiodic inspection policy produces lower maintenance cost.

Maintenance, production and quality are strongly linked to each other. Predictive maintenance is an effective way to eliminate the potential failures and ensure the stable operation of the manufacturing system, and further improve the reliability of the manufacturing system and the quality of manufactured products. Regarding the intelligent manufacturing philosophy “prediction and manufacturing#x201D;, a novel predictive maintenance strategy is proposed, in which the product quality control is integrated into predictive maintenance decision-making. First, the key process variables that characterize equipment wear are identified and integrated into the modeling of the equipment failure rate. Second, quality deviation that characterizes product quality level is defined based on co-effect between manufacturing system component reliability and product quality (i.e., Q-R chain). Third, the optimal maintenance strategy is obtained by optimizing the quality cost, maintenance cost, and interruption cost simultaneously. Finally, a case study on the predictive maintenance strategy decision-making for a cylinder head manufacturing system is presented to illustrate the effectiveness of the proposed method. The final result shows that the proposed method can achieve a superior economic performance relative to the conventional preventive maintenance mode.

An imperfect age-based and condition-based opportunistic maintenance model for a two-unit series system

With the popularization of service-oriented manufacturing, the current operation & maintenance (O&M) has shifted from traditional in-house maintenance to proactive outsourcing maintenance. It is paramount for an original equipment manufacturer (OEM) to provide timely and cost-effective maintenance schemes to geographically distributed customer enterprises. Interestingly, transportation theories could be combined to facilitate multi-location O&M management. In this paper, a transportation-oriented cross-regional opportunistic maintenance (TCOM) policy is developed for solving O&M optimizations and planning real-time schemes for the multi-center service network. The optimization model of this TCOM policy addresses several inter-related decisions: (1) most suitable maintenance times for each leased machine, (2) cost-effective arrangements of technician teams to perform maintenance tasks, and (3) optimal service routes for required teams. We not only integrate maintenance grouping and technician routing, but also investigate the new issues arising from the collaborative sharing of technician teams belonging to different maintenance centers. Numerical examples show that this TCOM policy can achieve significant cost-saving in cross-regional maintenance grouping and multi-location routing optimization for OEMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical problem of computing an optimal maintenance policy for the multi-center service network of geographically distributed production systems to achieve a timely and cost-effective O&M management. We consider individual machine degradations, complex maintenance opportunities and network logistics optimization to establish a global maintenance scheme for the multi-center service network. The existing approaches are to schedule the preventive maintenance (PM) for a single machine or multi-unit leased system, which is unilateral and cannot be applied to multi-location production systems. Although the network opportunistic maintenance policy has been proposed in the literature for maintenance grouping and technician routing problem, it still does not consider the limited workforce resource and the collaborative sharing of technician teams from different maintenance centers. To fill this literature gap, in this paper, we develop the TCOM policy to determine the optimal maintenance timetable for leased machines, as well as the cost-effective service route of technician teams belonging to multiple maintenance centers, so as to conduct timely and cost-effective PM on multi-location production systems. Compared with the conventional maintenance policy, our adaptive policy greatly reduces the total outsourcing maintenance cost in long-term O&M services.

In the traction power supply system (TPSS) of the high-speed railway (HSR), the traction load produces extremely severe load conditions. Therefore, Gas insulated substation (GIS) equipment served in TPSS of HSR suffers from both progressive degradation and random shocks. In this paper, the failure model of GIS equipment is considered as a competing failure mode multiplied by these two failure mechanisms. The degradation process and the shock process are described by gamma process and compound Poisson process, respectively, based on their temporal and spatial randomness. Furthermore, the maintenance strategy for GIS equipment in long-run time span with periodic inspections is developed. Under comprehensive consideration of optimal reliability and economic efficiency, indices associated reliability and maintenance cost are defined to evaluate the maintenance strategy performance in long-run time span for GIS equipment. The analysis results in the case study show that the inspection period and the shock threshold, as two maintenance decision parameters, can be optimized to achieve both high reliability and low maintenance cost. The influences of failure model parameters on optimized maintenance strategy are also revealed.

This study addresses the limitations of existing maintenance decision-making approaches that predominantly rely on single-objective strategies for leased production systems with complex series–parallel configurations. An integrated opportunity-based adaptive maintenance strategy is proposed, and a multi-objective optimization model incorporating multiple maintenance alternatives is developed. First, a proportional hazards model to characterize the degradation-dependent failure rates of key components is used to characterize equipment failure rates, which inform the selection of maintenance actions. Second, the effects of virtual age and maintenance strategies on the residual value of leased equipment are analyzed, leading to the formulation of a net residual value model from the lessor’s perspective. Simultaneously, a customer cost model is established by considering both product quality loss and downtime loss. Finally, the NSGA II algorithm is employed to solve the proposed multi-objective optimization model, yielding optimal preventive maintenance intervals, opportunistic maintenance thresholds, preventive maintenance thresholds, and the corresponding Pareto front. A case study illustrates the strategy’s superior flexibility and practical applicability, with its effectiveness further validated through comparative analysis against traditional maintenance strategies.

Reliability optimization of wind farms considering redundancy and opportunistic maintenance strategy

This paper proposes a way to simultaneously optimise three aspects of maintenance strategies for wind farms: the reliability thresholds that indicate when a component needs maintenance, the priority of maintenance jobs in case there are more jobs than available maintenance teams, and the use of opportunistic maintenance. We use a multi-objective evolutionary algorithm to simultaneously minimise the overall maintenance cost and the system idle time. An empirical study using a complex stochastic simulation demonstrates the benefit of such a multifaceted maintenance strategy over strategies that only consider some aspects.

Reliability analysis for complex electromechanical multi-state systems utilizing universal generating function techniques

Power electronic converters are one of failure sources in energy systems, and hence drivers of downtime costs in power systems. Different approaches can be employed for converter reliability enhancement including design/control for reliability methods, condition monitoring and fault diagnosis, and maintenance strategies. This paper proposes optimal preventive maintenance strategies based on wear-out failure model of converter components. The proposed approaches employ two different performance measures at converter-level and system-level. The converter-level measures take into account planned and unplanned maintenance times or costs in a single unit or small-scale system. Moreover, the system-level measure considers not only maintenance times, but also energy losses and additional maintenance costs induced by aging of the converter components. The outcome is optimal replacement time of converter and its components, which depends on the employed performance measure. Optimal replacement scheduling is of importance for risk management and decision-making during planning of modern power electronic based power systems. The applicability of the proposed approaches is illustrated by numerical analysis in a photovoltaic system.

Proactive preventive maintenance policy for buffered serial production systems based on energy saving opportunistic windows

Optimal maintenance strategy for corroded subsea pipelines

Power equipment usually goes through multiple states during the transition from normal state to the failure state. In order to consider equipment multistate property, an opportunistic maintenance optimization model based on multistate Markov process is proposed considering economic dependence within the transmission bay. This model describe equipment state transition process using the multistate Markov process. And then, opportunistic maintenance strategy between dependent power equipment in a transmission bay is studied. The transmission bay availability is derived considering opportunistic maintenance and scheduled maintenance. Finally, system operation cost is quantified. System operation cost is minimized to optimize equipment maintenance strategies. Numerical studies demonstrate validity of the proposed model.