Maintenance Cost Estimation Research Articles

Uncertainty quantification in hydrogen tank exchange: Estimating maintenance costs for new aircraft concepts

The increasing demand for sustainable air mobility has led to the development of innovative aircraft designs, necessitating a balance between environmental responsibility and profitability. However, despite technological advancements, there is still limited understanding of the maintenance implications for hydrogen systems in aviation. The aim of this study is to estimate the maintenance costs of replacing the hydrogen storage system in an aircraft as part of its life cycle costs. To achieve this, we compared conventional and hydrogen-powered aircraft. As there is insufficient data for new aircraft concepts, typical probabilistic methods are not applicable. However, by combining global sensitivity analysis with Dempster–Shafer Theory of Evidence and discrete event simulation, it is possible to identify key uncertainties that impact maintenance costs and economic efficiency. This innovative framework offers an early estimate of maintenance costs under uncertainty, enhancing understanding and assisting in decision-making when integrating hydrogen storage systems and new aviation technologies.

Repair tolerance assessment for aircraft composite structures using Bayesian updating

Detection and repair of composite damage is crucial to ensure the safety and reliability of aircraft structures. A novel approach to quantitatively evaluate the repair tolerance of composite structures in civil aircraft based on Bayesian updating is presented. The method incorporates historical damage inspection data to determine the prior distribution of damage size, which is then updated with newly collected damage size data using Bayesian theory. Monte Carlo simulation is employed to investigate the probability of failure and estimate maintenance costs, considering various factors such as the frequency and timing of damage events, damage detection, structural strength, gust loads, and maintenance expenses throughout the lifecycle of composite structures. Safety and economic factors are considered to establish a lower threshold for repairs and an upper threshold for maintenance based on the occurrence of accidental impact damage. Verification of the effectiveness and feasibility of a quantitative assessment method for repair tolerance is conducted using damage statistics data from civil aircraft routes utilizing the structural skin panels of composite outer wing. The results demonstrate that the method proposed in conjunction with extensive simulations and full utilization of field damage inspection data can effectively simulate unexpected impact damage situations that may occur during civil aircraft service and evaluate the reliability and economic feasibility of the repair of structure. The research findings hold significant theoretical and practical value for the preparation of documents for continued airworthiness of composite structures, including structural repair manuals and maintenance programs.

Review and assessment of smartphone apps for forest restoration monitoring

With increased interest in forest restoration comes an urgent need to provide accurate, scalable, and cost‐effective monitoring tools. The ubiquity of smartphones has led to a surge in monitoring apps. We reviewed and assessed monitoring apps found through web searches and conversations with practitioners. We identified 42 apps that (1) automatically monitor indicators or (2) facilitate data entry. We selected the five most promising from the first category, based on their relevance, availability, stability, and user support. We compared them to traditional field techniques in a well‐studied restoration project in Costa Rica. We received further feedback from 15 collaborator organizations that evaluated these in their corresponding field restoration sites. Diameter measurements correlated well with traditional tape‐based measurements (R2 = 0.86–0.89). Canopy openness and ground cover showed weaker correlations to densiometer and quadrat cover measurements (R2 = 0.42–0.51). Apps did not improve labor efficiency but do preclude the purchase of specialized field equipment. The apps reviewed here need further development and validation to support monitoring adequately, especially in the tropics. Estimates of development and maintenance costs, as well as statistics on user uptake, are required for cost‐effective development. We recommend a coordinated effort to develop dedicated restoration monitoring apps that can speed up and standardize the collection of indicators and provide evidence on restoration outcomes alongside a centralized repository of this information.

A Review on the Future of SMR Reactors in Nuclear Energy

The development of small modular reactors (SMRs) has emerged as a promising solution to provide clean, affordable, and reliable energy as global energy demand continues to rise. With a focus on different reactor types, their designs, and implementation strategies, this study offers a thorough overview of the state of SMR technology. This article evaluates the potential and challenges of small modular reactors concerning nuclear energy. It also thoroughly reviews the advantages and disadvantages of SMR technology, identifies regulatory barriers, public opinion, and infrastructure deficiencies, and investigates how SMRs might work with renewable energy sources to reduce greenhouse gas emissions. The SMR industry also emphasizes the significance of ongoing research and development. Adopting SMR reactors can be made easier with the support of advancements in design, technology, and regulatory frameworks. The integration of SMRs with renewable energy sources has the potential to provide a dependable, low-carbon, and sustainable energy solution. Advances in design, technology, and regulatory frameworks can facilitate the adoption of SMR. Annual operating and maintenance cost estimates can be affected by a wide range of additional factors, including the location of a plant, its capacity factor, regulatory considerations, plant obsolescence, and the number of reactors on the site. The difference in average cost ($/KWh) between SMR and Large-size reactors is about 20

Leveraging industry 4.0 techniques for predictive equipment maintenance: From concept to commissioning

In recent decades, lean manufacturing has significantly impacted the manufacturing industry, gaining widespread adoption. Companies have increasingly recognised the competitive advantages of implementing flow-oriented production layouts, demand-flow technologies, and just-in-time production. This shift has also transformed the approach to maintenance, moving away from reactive strategies. With production cells becoming more susceptible to system disturbances, maintenance managers are now focused on strategic maintenance development to ensure reliable production equipment. The emergence of Industry 4.0 technologies has further reshaped plant maintenance, providing companies with accurate and dependable tools for proactive maintenance. This paper proposes a hypothesis of research conducted at the University of Malta, focusing on the potential application of predictive maintenance (PdM) in the upkeep of automation machinery. It suggests the use of a novel data management and acquisition system, grounded in simulation and deep learning modelling, to predict the remaining useful life (RUL) of machinery early during the design machine realisation. This exploration lays the groundwork for potential development of a comprehensive maintenance tool. Such a tool would optimise automation designs, estimate maintenance costs throughout the machine’s lifecycle, and prevent machine breakdown through proactive interventions.

Modeling the Optimal Maintenance Strategy for Bridge Elements Based on Agent Sequential Decision Making

In addressing the issues of low efficiency in bridge maintenance decision making, the inaccurate estimation of maintenance costs, and the lack of specificity in decision making regarding maintenance measures for specific defects, this study utilizes data from regular bridge inspections. It employs a two-parameter Weibull distribution to model the duration variables of the states of bridge elements, thereby enabling the prediction of the duration time of bridge elements in various states. Referring to existing bridge maintenance and repair regulations, the estimation process of maintenance costs is streamlined. Taking into account the specific types and development state of bridge defects, as well as considering the adequacy of maintenance and the restorative effects of maintenance measures, an intelligent agent sequential decision-making model for bridge maintenance decisions is established. The model utilizes dynamic programming algorithms to determine the optimal maintenance and repair measures for elements in various states. The decision results are precise, all the way down to the specific bridge elements and maintenance measures for individual defects. In using the case of the regular inspection data of 222 bridges along a highway loop, this study further validates the effectiveness of the proposed research methods. By constructing an intelligent agent sequential decision-making model for bridge element maintenance, the optimal maintenance measures for 21 bridge elements in different states are obtained, thereby significantly enhancing the efficiency of actual bridge maintenance and the practicality of decision results.

Building Maintenance Cost Estimation and Circular Economy: The Role of Machine-Learning

The building industry generates large amounts of solid waste due to construction, maintenance, or demolition activities. Although the construction and demolition waste is well-documented in the literature, the maintenance phase could benefit from more academic research. Maintenance waste could be managed based on a linear economy or a circular economy. Since the existing linear economy poses significant problems, a transition to a circular economy is necessary. Such a transition is in its infancy stage and is hindered by multiple barriers. This paper developed a practical methodology for this transition and demonstrated its advantages to encourage its adoption by the building industry. These advantages included increasing the accuracy of building maintenance cost estimation, reducing the possibility of maintenance cost over-estimation, and reducing the waste of maintenance resources. These advantages were mostly due to replacing the deterministic and probabilistic maintenance cost estimation models with a machine-learning-based methodology. This methodology was shown to be versatile in several areas of application, i.e. anomaly detection, feature engineering, cost estimation, and validation. The methodology could be applied to any building type in any geographic location. It also facilitates achieving sustainable development goals #9 (through innovative building maintenance), #11 (via promotion of welfare-oriented building services), and #12 (by encouraging responsible consumption of maintenance resources).

Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels

The maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alternative fuels such as natural gas and propane has fostered the natural gas and propane powertrain systems as well as electrification options for heavy- and medium-duty vehicles. A critical barrier to adopting alternative fuel vehicles has been the lack of knowledge on comparative vehicle maintenance/repair costs with conventional diesel. Moreover, the region of operation, the type of vehicle operation, and seasonal temperature changes also affect the duty cycle which impacts the maintenance and repair costs. This study focuses on estimating the cost-per-mile for heavy-duty vehicles using machine learning models such as random forest, xgboost, neural networks, and a super-learner model. The super-learner model achieved an error as low as 0.0068 $/mile for mean absolute error and 0.0086 $/mile for root mean square error with a coefficient of determination/R-Squared of 97.28%. Specifically, the paper investigates the data collected from the maintenance and repair costs associated with delivery trucks using diesel and natural gas fuels. Since the availability of data is the major constraint, we leveraged the data collected by West Virginia University and the partnership with fleet companies. This allows for additional information related to maintenance costs and fleet-specific maintenance practices of alternative fuel vehicles. This study promotes clean fuel technologies and enables fleet management companies to adopt alternative fuel vehicles in case of similar or lower cost of maintenance compared to diesel vehicles resulting in reduced emissions and total cost of ownership.

Analisa Tingkat Kerusakan Dan Estimasi Biaya Perawatan Bangunan Gedung Utama Sekolah Tinggi Teknologi Indragiri

Building as an educational service requires maintenance so that the condition of the building remains fit for function. Without building maintenance activities, the function of a building will experience degradation over time. In this study, researchers focused on research on the Main Building of the Indragiri High School of Technology which was more than 10 years old to analyze the level of damage to the building and estimate the cost of maintaining the building. In the calculation that the level of damage is calculated based on the Regulation of the Minister of Public Works Number 24/PRT/M/2008 concerning Guidelines for the Maintenance and Care of Buildings, from the sum of all the components, 89.0% is categorized as Severely Damaged with the greatest damage in structural damage. The estimated maintenance cost for the main building of the Indragiri High School of Technology is Rp. 467,929,000.00 including Four Hundred Sixty Seven Nine Hundred Twenty Nine Thousand Rupiah
 

Estimation of intervention costs on urban pavements depending on the data acquisition method

The maintenance of the road network in Brazilian municipalities is commonly carried out through emergency repairs and resurfacing, whose effectiveness is questionable. The decision-making process can be improved by implementing and continuously using an Urban Pavement Management System - UPMS. This research aims to analyze the influence of the pavement condition data acquisition method on the estimation of maintenance and rehabilitation costs. Assessments were carried out by walking, considered as a reference by the main road agencies, and by filming with a camera fixed to the outside area of a car. In both cases, the same form was filled out. The study site was the municipality of Valparaíso de Goiás, in the state of Goiás, Brazil. Using the video-registration method, the budgetary need for the 78 evaluated segments of pavement was R$2,270,569.60, and through walking surveys, it was R$2,767,870.40, 21% more, demonstrating the impact of the walking survey method on the estimation of M&R costs.

Estimating Maintenance Costs of Sewer System

Maintenance costs of all types of buildings are most often ignored since they are incurred in the future. Potential investors are interested in capital costs—construction costs—while maintenance costs are considered as unimportant in the life cycle of a building. If there were a larger number of maintenance cost estimation models, it would be possible to estimate these costs and present them to potential investors more easily, thus making it easier and more effective to apply life cycle cost methods. A study on the characteristics and costs of the maintenance of sewer systems in the Republic of Croatia was conducted, wherein questionnaires were sent to companies operating public sewer systems. The data requested in the questionnaires were general data on enterprises, maintenance, data on sewer systems, quantities of sewer discharge, sewer pipes and data on maintenance costs of sewer systems. It was established that it is possible to use linear regression when creating a model for estimating the maintenance costs of a sewer system.

Cognitive data imputation: Case study in maintenance cost estimation

Cost estimation is critical for effective decision making in engineering projects. However, it is often hampered by a lack of sufficient data. For this, data imputation techniques can be used to estimate missing costs based on statistical estimates or analogies with historical data. However, these techniques are often limited because they do not consider the existing knowledge of experts. In this paper, a novel cognitive data imputation technique is proposed for cost estimation that uses explanatory interactive machine learning to integrate and improve human knowledge. Through a case study in maintenance cost estimation the effectiveness of the approach is demonstrated.

Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels

Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

Recommendation of Regression Techniques for Software Maintainability Prediction With Multi-Criteria Decision-Making

Context: Successful project management requires accurate estimation of maintenance effort and cost. Software Maintainability Prediction (SMP) plays a very important role in controlling software maintenance costs by detecting software modules with low maintainability. In previous research, numerous regression techniques were applied to predict software maintainability. The results with respect to various accuracy or performance measures are conflicting. Thus, there is a dire need to develop a method that can recommend regression techniques for predicting software maintainability in the presence of conflicting performance or accuracy measures. Objective: This paper aims to recommend suitable regression techniques for SMP based on the Multi-Criteria Decision-Making (MCDM) approach. Methodology: In our proposed approach, selecting a regression technique for SMP is modeled as the MCDM problem. To validate the proposed approach, an empirical study is done using three MCDM methods, 22 regression techniques, and eight performance measures over five software maintainability datasets. Before applying MCDM methods, a statistical test, namely the Friedman test, was conducted to ensure the significant difference between regression techniques. Results: The results of our study show that SVR, IBK, REPTree, and MLP-SVM achieve the highest-ranking score value one and are recommended as top-ranked approaches for SMP based on MCDM rankings. Conclusion: The main outcome of this study is that the proposed MCDM-based approach can be used as an efficient tool for selecting regression techniques among different available regression techniques for SMP modeling in the presence of more than one conflicting accuracy or performance measure.

Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems

Currently, in manufacturing, massive useful data about health condition and maintenance is often available thanks to Industry 4.0 technologies. However, how to take advantage of historical data to optimize maintenance policies for multi-component systems has still been a challenging problem. This is especially true when maintenance cost models at component level are not available and/or maintenance actions are imperfect. In order to cope with this issue, we propose in this paper an artificial-intelligence-based maintenance approach which first constructs a predictor based on artificial neural network (ANN) for estimating maintenance cost at system level and then employs a customized multi-agent deep reinforcement learning algorithm to optimize maintenance decisions that can be applied for large-scale systems. To evaluate the performance and scalability of the proposed maintenance approach, numerical studies are conducted on a small 4-component system with different configurations and a large system composed of 15 components considering both deterministic and random maintenance quality. The simulation results show that ANN-based predictor is efficient for maintenance cost forecasting and multi-agent deep reinforcement learning is a promising solution for maintenance decision-making and optimization.

Cost-benefit analysis of mesophyll conductance: diversities of anatomical, biochemical and environmental determinants.

Plants invest photosynthates in construction and maintenance of their structures and functions. Such investments are considered costs. These costs are recovered by the CO2 assimilation rate (A) in the leaves, and thus A is regarded as the immediate, short-term benefit. In photosynthesizing leaves, CO2 diffusion from the air to the carboxylation site is hindered by several structural and biochemical barriers. CO2 diffusion from the intercellular air space to the chloroplast stroma is obstructed by the mesophyll resistance. The inverses is the mesophyll conductance (gm). Whether various plants realize an optimal gm, and how much investment is needed for a relevant gm, remain unsolved. This review examines relationships among leaf construction costs (CC), leaf maintenance costs (MC) and gm in various plants under diverse growth conditions. Through a literature survey, we demonstrate a strong linear relationship between leaf mass per area (LMA) and leaf CC. The overall correlation of CC vs. gm across plant phylogenetic groups is weak, but significant trends are evident within specific groups and/or environments. Investment in CC is necessary for an increase in LMA and mesophyll cell surface area (Smes). This allows the leaf to accommodate more chloroplasts, thus increasing A. However, increases in LMA and/or Smes often accompany other changes, such as cell wall thickening, which diminishes gm. Such factors that make the correlations of CC and gm elusive are identified. For evaluation of the contribution of gm to recover CC, leaf life span is the key factor. The estimation of MC in relation to gm, especially in terms of costs required to regulate aquaporins, could be essential for efficient control of gm over the short term. Over the long term, costs are mainly reflected in CC, while benefits also include ultimate fitness attributes in terms of integrated carbon gain over the life of a leaf, plant survival and reproductive output.

Avoiding the effects of translocation on the estimates of the metabolic rates across an elevational gradient.

Body maintenance costs are often considered a proxy for performance in fitness traits. Maintenance energy requirements are measured as minimal metabolic rate of inactive, postabsorptive individuals in the laboratory. For mountain-dwelling species, translocation to the laboratory often means that they are also moved to another elevation. Due to physiological adaptations to local oxygen pressure, rapid elevational change can alter metabolic rate and translocation may result in erroneous estimates of body maintenance costs. In this study, we measured resting metabolic rate (RMR) of three populations of the Mesquite lizard (Sceloporus grammicus, Wiegmann 1828) at their native elevations (i.e., 2600, 3200 and 4100m). Our results showed that at native elevations, mass specific RMR of lizards from the high elevation population (4100m) did not differ from the RMR of the other populations (i.e., 2600 and 3200m), whereas the lizards from the low elevation (2600m) had lower RMR than those from the intermediate population. These results differ from a previous study in which the RMR of lizards from the same populations were reported to increase with native elevation when translocated and measured at an intermediate elevation. Hence, our results show that translocation in elevation can affect metabolic measures. We caution researchers that changes in elevation may preclude accurate measures of RMR in some animals and may therefore incorrectly predict performance of fitness-related traits.

Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models

Although various tools and procedures have been developed to help transportation engineers objectively evaluate bridge maintenance needs, selecting and scoping projects still relies on engineering judgment. The present paper intends to help engineers evaluate and maintain bridges by developing a comprehensive bridge maintenance planning framework (BMPF) within financial and performance constraints. The paper's objectives are to maximize the performance condition level of bridges and to minimize the maintenance cost by optimally planning the maintenance treatments. The framework includes bridge performance impact assessment, machine learning models, multi-attribute utility theory ranking model and genetic algorithm optimization model. The study analysed 95 bridges in a network with an 84% accuracy machine learning model prediction. Decision-makers' preferences were considered to rank all bridges using Multi-Attribute Utility Theory. 19 bridges were then chosen for maintenance based on budget and performance using a genetic algorithm model. The BMPF was observed to improve project productivity, reduce down time, and improve bridge inventory condition. Future research can explore the use of other optimization approaches and also include traffic flow and construction cost analysis for a better maintenance cost estimation. The machine learning model for performance prediction can be enhanced by utilizing different techniques.

Performance comparison of Putnam model using new technology trends for software maintenance cost estimation

Now the concept of software estimation is added by concrete concepts of fuzzy logic and neural networks that help in monitoring as well as understanding the software development dynamic process. The goal of all these is to estimate the estimating software effort. Approximate maintenance and judgment costs for a project is defined by software maintenance cost estimation. Due to environmental, human, political and technical like number of involved variables for cost estimate calculation, the maintenance cost estimation will never be same. In this paper we have proposed a new approach using Putnam model which is an efficient software maintenance cost estimation model. For dataset acquisition we have considered Bug Id reports instances of Matlab to perform classification of Sentimental based on severity. After pre-processing the dataset Principal Component Analysis (PCA) has been used to compute and study the Eigenvector for feature extraction. Then instance selection is performed using Particle Swarm Opitmization. In order to reduce matrix size there is need to deal with instances selection that will make processing easy to deal with further proceeding input. Once an instance is selected the next step is to classify it. In this work we have used Linear Discrement Analysis (LDA) as classifier which is an efficient statistical method used in machine learning. For testing the proposed approach we have compared the results obtained using COCOMO and PUTNAM model that shows the use of Putnam model will improve the system in terms of various parameters.