Optimal Maintenance of Below-Grade Culverts

Leasing has proven to be a business model that is perfectly suited to the circular economy. It significantly contributes to sustainable development by enabling the reuse of machinery and equipment after each lease period and by including preventive maintenance and overhauls within and between lease terms. This helps to extend the life cycle of equipment, promote value recovery, and reduce waste. This paper examines an imperfect preventive maintenance (PM) strategy applied to equipment rented under the terms of “free leasing”. In free leasing, the lessor makes the equipment available to the customer for a specified period of time without charging rent. In return, the customer is required to purchase the equipment’s consumables exclusively from the lessor. The lessor is also responsible for the maintenance of the equipment at the customer’s premises. The greater the quantity of consumables used by the customer, the more the equipment will deteriorate. Consequently, the lessor must be able to determine the most effective approach to preventive maintenance, ensuring that it aligns with the customer’s planned usage rate while maximizing profit. This work proposes a PM strategy to be adopted by the lessor during the free lease period. This strategy involves the performance of imperfect PM actions just before the start of the lease period and then periodically. Different packages of preventive actions can be applied each time, with each package having a different cost depending on the level of effectiveness in terms of rejuvenating the equipment. Minimal repairs are performed in the event of equipment failure. The decision variables are the PM period to be adopted and the maintenance efficiency level to be chosen for each preventive intervention. The objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables so that the lessor maximizes their profit. A mathematical model is developed to express the lessor’s average profit over each lease period. A solution procedure is developed for small instances of the problem, and an Artificial Bee Colony algorithm is implemented for larger instances. A numerical example and a sensitivity analysis are presented.

To provide a comprehensive foundation for maintenance strategy decision-making, this study integrates Building Information Modeling (BIM), Environmental Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA) to perform automated Net Present Value (NPV) analysis by monetising the multidimensional impacts of various maintenance strategies. A case study on provincial roads in China shows that preventive maintenance strategies, especially those using emerging technologies like Ultra-Thin Overlay (UTO), are more cost-effective than traditional strategies and should be prioritised when conditions allow. Additionally, the study identifies the optimal maintenance frequencies for each strategy: once every 5 years for the UTO strategy, once every 9 years for the Milling and Filling (M&F) of the Surface Course strategy, and once every 7 years for the Hot In-Place Recycling (HIR) strategy. In summary, the BIM-LCA-LCCA approach offers a versatile, efficient, and real-time method for supporting decision-making in pavement maintenance strategies. It is applicable to most mainstream asphalt pavement structures in China and is expected to provide valuable insights for optimising the design and management of future infrastructure projects.

Roads constitute an essential infrastructure that supports mobility and regional development. As the service life increases and traffic loads intensify, road performance may deteriorate, thereby affecting traffic flow and the level of service. This study aims to determine the current condition of the pavement on Guru Tua Road along the Palu–Kulawi road section through an assessment using the Pavement Condition Index (PCI) method, identify alternative road maintenance strategies basedS on the PCI value of each sample unit, analyze the life cycle cost (LCCA) for each maintenance strategy alternative on Guru Tua Road along the Palu–Kulawi section, and recommend an optimal maintenance strategy that aligns with the road condition and cost efficiency over the design life. Data were collected through road condition surveys and planning data, which were subsequently analyzed using the Pavement Condition Index (PCI) method to determine the road condition classification. Furthermore, the Life Cycle Cost Analysis (LCCA) method was employed to evaluate alternative maintenance strategies. The results of the Pavement Condition Index (PCI) evaluation indicate that Segment 1 (Km 0+000–1+300) has an average PCI value of 78.88 and Segment 2 (Km 1+300–2+600) has an average PCI value of 75.43, both categorized as fair condition, thus periodic maintenance Sis recommended. Segment 3 (Km 2+600–3+900) has an average PCI value of 68.45, which falls into the poor condition category, therefore rehabilitation measures are required. For each segment, three treatment alternatives were developed and subsequently analyzed using the Life Cycle Cost Analysis (LCCA) method. In Alternative 1, the Equivalent Uniform Annual Cost (EUAC) at a discount rate of 8% with a Capital Recovery Factor (CRF) of 0.102 shows that Alternative 1 has a Present Value (PV) of IDR 13,655,411,431, with an Equivalent Uniform Annual Cost (EUAC) of IDR 1,390,833,816.62 and an EUAC per square yard of pavement of IDR 56,648.49. This alternative yields the lowest life cycle cost and annual cost over a 20-year design life.

In view of the inadequacy of research on large-scale system maintenance strategies from the perspective of life cycle, this study proposes a new method for optimizing maintenance strategies in a large-scale system with homogeneous units that degrade over time. Specifically, assuming the unit failure obeys Weibull distribution, we consider the optimization model of imperfect grouping maintenance construct under the system reliability constraint. In the optimization model, the system life cycle cost is taken as the optimization target, and the preventive maintenance cycle and unit reliability are taken as the decision variables. Genetic algorithm was used to obtain the global optimal combination of decision variables at different maintenance improvement factor levels. The analysis results of typical examples show that the maintenance improvement factor is an important parameter that affects the life-cycle cost and reliability of the system. The results of this study can greatly simplify the decision-making process of engineers, and are more applicable to the formulation of maintenance strategies for large-scale systems requiring regular maintenance.

Customer Integration to Gain Cost Efficiency Alongside Tool's Life Cycle

Linking maintenance strategies to performance

Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology; in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process.

<p>Bridges are highly complex structures exposed to heavy environmental and mechanical loads, which require immense financial investments in all life cycle periods, from planning, design, operation and end of life phase. The longest period in whole life cycle of a bridge is operational phase which includes numerous maintenance and repair activities. Timing of these activities and their extent is a crucial factor that influences bridge future performance.</p> <p>Traditionally, the decision to perform maintenance is based on the infrastructure managers' observations, judgments and choices which are derived from inspection results, available budgets and abrupt failures. Though, maintenance based on these drivers very often leads to undue maintenance with increased cost. For this reason, predictive maintenance is considered to be a most effective maintenance policy that suggests to perform maintenance only where it is promptly needed. However, predictive maintenance poses decision-making challenges for infrastructure managers. Particularly for road infrastructure networks, the maintenance decision-making is a difficult task due to a widespread network of diverse infrastructure objects, availability demands, deterioration rate and budget constraints. Such infrastructure maintenance requirements pose decision-making dilemmas to the infrastructure managers, where maintenance planning is challenged by the number of conflicting issues (e.g. limited budget vs. aging network, the risk of failures vs. traffic intensity over an object).</p> <p>This paper tries to examine the effect of two opposing maintenance strategies for life cycle management of bridges, corrective and preventive maintenance based on historical data about bridges in three countries and 30 years of experience. The maintenance costs for bridge structural elements at different condition levels are analysed, based on the data from three countries, Croatia, Slovenia and the Netherlands, where different approaches to maintenance strategies are being applied. The overall maintenance costs over a longer period of time, up to 30 years are collected and analysed, with the aim to clearly define pros and cons for both approaches.</p>

Modern medical equipment and devices have become very sophisticated and complex. These assets are expected to operate under rigorous environments. Hospitals need to confirm that their critical assets particularly medical equipment are reliable, safe, accurate and operating with high level of performance. However, activities of maintenance, condition monitoring, inspection and maintenance optimization models are fairly new to medical devices. Current maintenance strategies in health sector have difficulty in applying the optimal maintenance strategy. This paper addresses clear gap based on literature review for medical equipment maintenance and condition monitoring. Implementing right maintenance strategy can lead to detect faults before they occur, so that preventive maintenance can be optimized through predication and analysing failure patterns. This paper aims to critically identify the predicting Remaining useful life (RUL) for hospital’s Gas-Pipe based on degradation analysis. According to previous studies, there is almost no research has been tackled this method of analysis in the medical device, especially in the gas pipeline system. The present research described the use of degradation data to estimate failure time in terms of P-F interval, which it was 8 years. Results of this paper show that the majority of gas-pipes reached the critical point less in than 15 years. While the manufacture specify the life-time between 15–20 years. In addition, this research contributed to the knowledge through recommended a developed framework in order to improving the facilities and assets management by increase the life time of medical devices, reduced the failure of critical devices, reduced the maintenance costs, and predict the critical failures before occurring.

Making the correct maintenance strategy decision for industrial multistage machines (MSTM) is a constant challenge for industrial manufacturers. Preventive maintenance strategies are the most popular and provide interesting results but cannot prevent unexpected failures and consequences, such as time lost production (TLP). In these cases, a predictive maintenance strategy should be used to maintain the appropriate level of operation time. This research aims to present a model to identify the component that failed before its mean time to failure (MTTF) and, depending on whether the cause of the failure is known, propose the use of a predictive maintenance strategy and further decision-making to ensure the highest possible value from operating time. Also, it is necessary to check the reliable value of MTTF before taking certain decisions. For this research, a real case study of a MSTM was characterized component by component, setting the individual maintenance times. The initial maintenance strategy used for all the components is the preventive programming maintenance (PPM). If a component presents an unexpected failure, a method is proposed to decide whether the maintenance strategy should be changed, adding a predictive maintenance strategy to monitor said component. The research also provides a trust level to evaluate the reliable value of MTTF of each component. The authors consider this approach very useful for machine manufacturers and end users.

We suggest a 2D-plot representation combined with life cycle greenhouse gas (GHG) emissions and life cycle cost for various energy conversion technologies. In general, life cycle assessment (LCA) not only analyzes at the use phase of a specific technology, but also covers widely related processes of before and after its use. We use life cycle GHG emissions and life cycle cost (LCC) to compare the energy conversion process for eight resources such as coal, natural gas, nuclear power, hydro power, geothermal power, wind power, solar thermal power, and solar photovoltaic (PV) power based on the reported LCA and LCC data. Among the eight sources, solar PV and nuclear power exhibit the highest and the lowest LCCs, respectively. On the other hand, coal and wind power locate the highest and the lowest life cycle GHG emissions. In addition, we used the 2D plot to show the life cycle performance of GHG emissions and LCCs simultaneously and realized a correlation that life cycle GHG emission is largely inversely proportional to the corresponding LCCs. It means that an expensive energy source with high LCC tends to have low life cycle GHG emissions, or is environmental friendly. For future study, we will measure the technological maturity of the energy sources to determine the direction of the specific technology development based on the 2D plot of LCCs versus life cycle GHG emissions.

Dynamic preventive maintenance strategy for MMC considering multi-term thermal cycles

High operational costs and downtime risks can affect industry efficiency making equipment maintenance optimization an urgent need. PT. IAA AMO, as a manufacturing industry in the field of aircraft maintenance, faces significant challenges with the low reliability of Ground Power Unit (GPU) engines which only reaches 86%. This has led to an increase in the company's operating costs. The purpose of this study is to optimize Preventive maintenance on the Ground Power Unit (GPU) in aircraft maintenance at PT. IAA AMO using the Failure Mode and Effect Analysis (FMEA) method. The results show that the MTTF of the GPU is 311 hours, while the MTBF is 373 hours. To prevent sudden breakdowns, preventive maintenance is recommended every 311 hours for non-repairable components and 373 hours for repairable components. This research offers a more effective maintenance strategy to improve GPU reliability and lower operational costs. This research results in more effective maintenance planning, which not only minimizes downtime and operational costs, but also improves the overall reliability of the GPU machine. With the implementation of FMEA methods and the right preventive maintenance strategy, companies can anticipate breakdowns early, extend engine life, and ensure smooth operations in support of aircraft maintenance.

The functional parts of a machine tool determine its reliability level to a great extent. The failure prediction of the functional part is helpful to prepare the maintenance scheme in time, in order to ensure a stable manufacturing process and the required production quality. Due to the rise of digital twin (DT), which has the characteristics of virtual reality interaction and real-time mapping, a DT-based real-time prediction method of the remaining useful life (RUL) and preventive maintenance scheme is proposed in this study. In this method, a DT model of the manufacturing workshop is established based on real-time perceptual information obtained by the proposed acquisition method. Subsequently, the real-time RUL of the functional part is predicted by establishing an RUL prediction model based on the nonlinear-drifted Brownian motion, which takes the working conditions and measurement errors into consideration. On this basis, the optimal preventive maintenance scheme can be determined and fed back to the manufacturing workshop, in order to guide the maintenance of relevant parts. Finally, an example case study is presented to illustrate the feasibility and effectiveness of the proposed method.

Jet engines are critical assets in aircraft, and their availability is crucial in the modern aircraft industry. Therefore, their maintenance scheduling is one of the major tasks an airline has to make during an engine’s lifetime. A proper engine maintenance schedule can significantly reduce maintenance costs without compromising the aircraft's reliability and safety. Different maintenance scheduling approaches have been used for jet engines, such as corrective, preventive, and predictive maintenance strategies. Regarding the safety demands in aircraft industries, preventive maintenance is a frequent maintenance method for jet engines. However, preventive maintenance schedules are often use fixed maintenance intervals, which is usually suboptimal. This paper focuses on minimizing a jet engine's overall maintenance cost by optimizing its preventive maintenance schedule based on an engine’s comprehensive reliability model. A hierarchical optimization framework including the golden section search and genetic algorithms is applied to find the optimal set of preventive maintenance number and their times and the components to be replaced at those times during the jet engine's overall lifetime. The Monte Carlo simulation is used to estimate the engine’s failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the engine's overall corrective and preventive maintenance costs during its lifetime. Finally, an optimal preventive maintenance schedule is proposed for an RB 211 jet engine using the presented method. In the end, comparing the proposed method's overall maintenance cost with two other maintenance methods demonstrates the proposed schedule's effectiveness. The method presented in this paper is generic, and it can be used for other similar engineering systems.