Maintenance Costs Research Articles

Ensemble Machine Learning Classification Models for Predicting Pavement Condition

Forecasting pavement performance condition is essential within the pavement management system to optimize decisions with regard to planning maintenance and rehabilitation projects. Accurate forecasts facilitate timely interventions and assist in formulating cost-effective asset management plans. Data-driven machine learning models that utilize historical data to improve forecasting precision have gained attention in the field of asset management. Although numerous studies have employed regression-based models to forecast pavement condition, transportation asset management often operates according to condition index ranges rather than exact values. Therefore, classification models are suitable for predicting pavement condition grades and determining the appropriate maintenance type for pavement assets. This research focuses on developing five machine learning classification models to predict pavement condition: random forest; gradient boost; support vector machine; k-nearest neighbors; and artificial neural network. To enhance prediction performance, these models are integrated using ensemble methods, including voting and stacking. The classification models are developed using a dataset from the Georgia Department of Transportation that documented the condition of asphalt pavements for predefined maintenance sections between 2017 and 2021. A voting ensemble model constructed with the two best-performing individual classification models reached the highest accuracy rate at 83%. Although the performance of individual models fluctuates, ensemble models consistently produce a top-tier performance regardless of the variations in data sampling. Therefore, ensemble methods are recommended for developing pavement condition prediction models to improve accuracy and achieve a more consistent quality of predictions. The findings of this research will provide transportation agencies with information to help them strengthen their forecasting practices in relation to pavement condition, thereby improving their maintenance planning and cost savings.

The preparation of electrically-enhanced and hydrophilic CNTs-PVDF composite hollow fiber membranes for fouling resistance

The anaerobic membrane bioreactor has great potentials to treat the wastewater with high organic matter due to the efficient anaerobic digestion process and high biomass retention. However, the existence of membrane fouling significantly increases the maintenance cost, and always requires frequent and complex cleaning process to recover the membrane performance. Thus, it is significant to fabricate a membrane with excellent anti-fouling performance for the application in membrane bioreactors. Herein, we prepared the CNTs-PVDF hollow fiber membrane with fouling resistance via loading the conductive CNTs coating on the surface of commercial PVDF membrane by a simple cross-linking reaction with hydrophilic polyvinyl alcohol and succinic acid. “Dual defense” was constructed by the combination of the hydrophilic surface and conductive CNTs coating, which lifted the anti-fouling performance of this membrane. In the assistance of electrical effect, the permeance of the membrane with −1.2 V bias could be recovered to 95 % of original state by simple hydraulic cleaning, while that of the membrane without bias was only 58 % after filtrating the sludge of 2000 mg/L. Furthermore, this membrane also exhibited the superior stability and fouling resistance in the operation of anaerobic electrochemical membrane bioreactor, which was promising for the advanced water treatment.

Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm

The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications.

Case study of condominium management in Brazil: survey of 30 years of maintenance and renovation costs of a 1982 multifamily residential building

Case study of condominium management in Brazil: survey of 30 years of maintenance and renovation costs of a 1982 multifamily residential building

Minimizing Flight Hours Losses by Utilizing Deep-Q Learning Techniques in Scheduling Aircraft Maintenance

Currently, there are insufficiently good or efficient tools or procedures for long-term scheduling of aviation maintenance activities in the world, and Vietnam specifically. Airlines are impacted by a variety of elements, including the quantity of aircraft they operate, their capacity, their personnel resources, their maintenance resources, and unforeseen and urgent occurrences that cause schedule disruptions. There are no fast, automatic solutions to the above-described problems existing in aviation today. The reinforcement learning method as described here could potentially be one answer to these problems. The idea is to plan in years running across a specified period such that the aircraft is brought as close as possible to the inspection deadline. From there, the airworthiness of the aircraft increases while the maintenance inspection decreases, reducing the cost of maintenance. Application optimization of the scheduling plan is done using the Deep Q-learning method. The results achieved by the Q-learning and Deep Q-learning algorithms are better in terms of computation times as compared to the other current techniques. The research results of the checks showed reinforcement learning potential in dealing with this problem, where the fly hours loss of planned inspections was reduced by using data from Vietnam Airlines. Computational experiments show that our methods adapt for different purposes and settings of reality. After teaching the model with these simulated conditions, they show how well a reinforcement learning application quickly arrives at lean repair plans.

Triple-net leased property portfolios and operational efficiency: Evidence from the U.S. REIT market

Triple-net leases are legal agreements by which tenants, beyond rent payments, are responsible for operating expenses such as real estate taxes, insurance, and maintenance costs. We examine the relative operational efficiency of Real Estate Investment Trusts (REITs) holding property portfolios consisting of majority triple-net leases. Results suggest REIT holding triple-net leased portfolios are more operationally efficient than their counterparts. Our results suggest that selecting proper lease expense provisions specifying who carries the obligation of operating expenses is a determining factor of REIT operational efficiency.

Enhancing the performance of zero energy buildings with boosted coyote optimization and elman neural networks

This research paper aims to optimize electrical system modeling to minimize CO2 emissions, improve reliability, and reduce costs. It proposes integrating Elman Neural Network (ENN) and metaheuristic algorithm to evaluate environmental impact and optimize system performance. The goal is to create a mathematical model considering renewable energy sources, grid energy, CO2 emissions, and maintenance costs, and use metaheuristic algorithms to determine the optimal combination of wind turbines and photovoltaic panels. To train the ENN model and optimize the system, a three-stage approach is proposed. Quasi-random numbers are generated using Sobol techniques to create input data, which is then processed by TRNSYS for simulation. The proposed approach incorporates an ENN with Boosted version of the Coyote optimization algorithm to accurately model and predict the behavior of Hybrid Renewable Energy Systems for Zero Energy Buildings. The results demonstrate the effectiveness of the proposed approach. Our method significantly reduces energy consumption, costs, and system reliability, achieving a 25% reduction in energy consumption and 12% reduction in system cost, and is effective in optimizing hybrid solar/wind and hydrogen storage systems. The optimal system configuration significantly reduces CO2 emissions, achieves a high level of system reliability, and minimizes costs. Comparative analysis with previous studies showcases the superiority of the proposed method in achieving comprehensive optimization. In conclusion, this study combines AI techniques and metaheuristic algorithms to optimize electrical system design, considering environmental, technical, and economic factors. The proposed approach overcomes limitations in previous studies and offers an effective solution to address the optimization problem.

Wind Power Integration and Challenges in Low Wind Zones. A Study Case: Albania

High wind performance systems are influenced by many factors such as site wind resources and configuration, technical wind turbine features and many financial conditions. Scenario planning and modelling activities often focus on restricted parameters and numbers to justify wind power plant performance. To better understand possible pathways to scaling up the distributed wind market in Albania, a deep and multidimensional calculations based on Monte Carlo analysis, using RETScreen and wind JEDI model, to assess socio-economic impact as a function of turbine output power, operating and maintenance cost and many other financial inputs by testing different WT (i.e., VESTAS, GAMESA, W2E and NORDEX) with rated power from 3.45 MW up to 4.5MW applied on LCOE, NPV, SPP, equity payback, B-C, after-tax IRR on equity and effects of GHG credits extended at a sensitivity range of ±35% is scientifically performed. From the simulation results LCOE reaches a minimal value of €43.48/MWh, if the debt rate is 99 % and a debt interest rate of 5.0%, a TotCapEx of €828/MW (-35 % less expenditures) indexed as the best scenario. For the base case scenario LCOE results €62.79/MWh, when applying a debt rate of 80% and a TotCapEx of (€1274/MW), while in the worst-case scenario LCOE impart a maximal value of €87.63/MWh if a TotCapEx of €1720/MW (+35 % more expenditures) and a share of 52 % debt rate is applied. Local annual economic impact (m€) during construction period and operating period evaluated in the wind JEDI model result around m€ 89.92 and m€ 23.54, respectively. As a conclusion, wind power plants (WPP), installed in low wind zones (Albania and many other EU countries) would be of interest if an electricity export rate of 110€/MWh, and a GHG credit rate of €50/tCO2 were accepted.

Public-private partnerships scheme of pioneer train case study in South Sumatera area

Pioneer trains in Indonesia have been fully funded by the government. The construction needs a lot of funding that would burden the state budget. One of the alternatives is the public-private partnership scheme. This study aims to analyze the most effective unbundling scenario for financing and maintaining the Indralaya-Tanjung Senai train. The life cycle costs and conducting sensitivity analysis according to applicable regulations. The scenarios are based on ticket price increases and government support for the operation and maintenance (O&M) for this pioneer train route, calculated net present value (NPV) and internal rate of return (IRR) value. Scenario 1 assumes that tickets do not receive subsidies; private companies are responsible for O&M costs. Scenario 2 assumes that ticket prices are set by private companies; the government is responsible for procurement and maintenance costs. Scenario 3 assumes that the government provides subsidies of IDR 10,000 per passenger as well as railway infrastructure; private companies bear the cost of procuring railbuses and operational expenses. Based on calculations with Scenario 1 NPV is IDR 0.73 billion; with Scenario 2 is IDR 4.64 billion and with Scenario 3 is IDR (–)1.34 billion. The analysis shows that increasing passenger fares according to price raises and inflation rates, and subsidies from governments for maintenance costs will make this railroad project financially feasible.

Development of a Low-Cost Web-Based Information System for Managing a University Department Chemical Warehouse

Herein, we present the implementation of a low-cost web-based information system tailored to manage a university department chemical warehouse. The system provides a centralized platform for cataloging, tracking, and managing chemical inventory data, while also facilitating purchasing and various administrative tasks associated with laboratory operations. The system has been developed within the Campusnet platform and has the following functionalities: (i) an efficient way for cataloging the extensive array of products available in the warehouse; (ii) an intuitive web interface with a device-responsive layout to facilitate browsing and purchasing of products by students, researchers, and technical staff; (iii) an inventory transaction recording system to simplify the attribution of costs; (iv) a simple procedure to streamline the process of joint purchases; and (v) a low deployment cost and ease of maintenance. Key design considerations, including regulatory compliance and safety, are also presented.

Investigation of two phase liquid jet ejector with simultaneous air and water suction in fresh water distillation system

Liquid jet ejectors can be utilized to generate vacuum in thermal desalinations, power plant applications and chemical industries. Liquid jet ejectors have no moving parts and have the advantage of reliability and low maintenance cost and also entrainment of non-condensable gas and brine simultaneously relative to pumps. The aim of this work is to model a liquid jet ejector with mixed air and water suction, applicable in fresh water distillers. The design is numerically simulated by two-phase Eulerian model and Realizable k-ε turbulence model. The results of the numerical model of liquid jet-air ejector are compared with experimental data and good agreement is achieved with deviation of about 7.4 % and 11.2 % for flow ratio and maximum discharge pressure respectively. In order to study robustness of the design, the value of suction entrainment and motive load of ejector is studied for different values of brine and suction pressure (8–12.5 kPa) of a 12 m3/day fresh water distiller. The results show constant air entrainment in off-design condition. In addition, mixing behavior and hydrodynamics of the flow in such ejector is studied by the parameters of pressure, velocity, flow and turbulent intensity distribution and compared with the ejector with air-only suction.

Evaluation of fracture indices of warm mix asphalt (WMA) modified with nano-additive under pure shear and pure tear deformations

Cracks located on the road edge and curve are two pavement crack types that can reduce road safety. Providing a solution to control this type of crack can solve a large part of the concern in this case. This study innovatively bridges the research gap by evaluating the efficacy of nano-reduced graphene oxide powder (NRGOP) in enhancing the fracture resistance of warm mix asphalt (WMA) under various environmental conditions, a critical aspect previously unexplored in the pavement engineering literature. For this goal, asphalt mixtures containing different ratios of NRGOP were constructed. Then, a level of aging and damage induced by freeze–thaw were applied to the semicircular bend (SCB) and edge-notched disc bend (ENDB) specimens to understand how the changes in fracture strength, fracture crispiness, and fracture stiffness are under modes III and II (at + 15 °C and −15 °C). The finding revealed that the introduction of NRGOP increased the fracture properties of WMA mixtures, such as fracture energy (FEII or FEIII) and fracture toughness (KIIC or KIIIC), as well as fracture stiffness under pure shear and pure tear deformations. Laboratory findings at low temperatures showed that it is necessary to investigate KIIC and FEII for asphalt mixtures under non-conditions and freeze–thaw conditions. However, KIIC and FEIII were suggested for aging conditions. Therefore, in two-thirds of the results, FEII was critical, and in 100 % results, KIIIC was critical. At + 15 °C, KIIC and FEIII were more crucial for non-treated and freeze–thaw conditions, while KIIC and FEII were more critical for aging, respectively. Therefore, in two-thirds of the results, FEIII was critical, and in 100 % results, KIIC was critical. The results of this research provide insights to advance the socio-economics of pavements by controlling surface cracks in tropical and subtropical regions, which in turn increases ride quality, driver safety, and pavement maintenance costs.

Coal-derived conductive pavement for winter de-icing: prototype, modeling, and simulation

Existing pavement de-icing methods result in high installation and maintenance costs, traffic delays, excessive weariness and corrosion, and negative environmental and safety impacts. To overcome these challenges, this paper presents an innovative pathway to designing and constructing smart self-heating pavements using a low-cost coal-char bearing asphalt material. The conductive asphalt incorporates coal char, i.e., a key byproduct of the coal pyrolysis process, into the Stone Mastic Asphalt (SMA) mixture. This asphalt containing coal-derived solid carbon exhibits highly tailorable electrical conductivity, satisfactory mechanical and thermophysical properties, and superior cost-efficiency as compared to other conductive pavement materials. The de-icing performance was also demonstrated by laboratory experiments on a bench-scale prototype. Furthermore, an efficient thermal network model was developed and validated by experiments to investigate the transient thermal behavior and energy performance of the Ohmic heating pavement system. The whole-year energy simulation case studies were conducted on a bridge pavement with an annual energy use of 24.6–1444.8 kWh/m2, showcasing its potential in field applications across cool humid, cold humid, and subarctic/arctic climate zones.

Sizing and energy management of grid-connected hybrid renewable energy systems based on techno-economic predictive technique

This study intends to construct and optimally run a grid-connected hybrid renewable energy system (HRES) powering a residential building. A special techno-economic multi-objective optimization technique is used for sizing the HRES. In addition, an energy management strategy (EMS) is established as a distinct multi-objective optimization problem to minimize degradation and operating costs while improving system performance. A Bi-level mixed integer non-linear programming (BMINLP) is the framework used to formulate the two-optimization problems. Sizing problem is characterized by the higher level, on contrast; the nested EMS problem within the constraints of the size problem is presented on the lower level. For this investigation, the problem is executed via the MATLAB program. The global optimization toolbox is applied via the Multi-Objective Genetic Algorithm (MOGA) to solve the sizing optimization. While the EMS is executed using the "Intlinprg" mixed integer linear programming (MILP) solver. The technical and economic performance parameters such as degradation, renewable energy fraction (REF), operating, maintenance, and investment costs are evaluated in this study. The primary results reveal that the summer week encounters the lowest total cost and the highest REF, at roughly 99484 $ and 60 %, respectively. Furthermore, the investigation shows that the lowest degradation value attained in the same week with approximately 50.

Implementation of Internet of Things for Condition-Based Maintenance in Elevator Systems: An Analytical Case Study

The aim of this paper is to show how IOT technology can allow highly distributed elevator equipment servicing by using remote-monitoring technology to ease a move from tradi- tional corrective maintenance (CM) and time-based maintenance (TBM) to more prophetic, condition-based maintenance (CBM) in evidence to accomplish different advantages.The Literature review can indicate that CBM has merit over conventional CM and TBM from a theoretical outlook, but it turns on constant monitoring enhancement via advanced IOT technology. A detailed case history was implement to bring practical proof that IOT allows elevator adamant to attain CBM.Through, a theoretical context, the CBM of elevators put together exper- tise.The venture lies in data collection, data analysis and decision making in real-world context of business. The key outcome of this research recommends that in the case of elevators the IOT can be commercialized and would improve the welfare and accuracy of equipment. It might, so, be understandable from technological, process and economic aspect. Our long term physical world case study reveal an actual way of building the CBM of elevators worldwide. Incorporating Internet of things and other modern technology will improve the safety and authenticity of elevator equipment,longer service life, minimize disruption or difficulty and business interference as a result of equipment downtime and to eliminate major repairs, thus result in low maintenance cost. A key contribution in this study stand in the empiric confirmation of the advantages and difficulties of CBM through Internet of things comparative to conventional CM and TBM in the instance of elevators. The writer had faith that this study is well timed and will be worth to company working on the same research or business strategies.These are form on condition based maintenance, IOT, Time-based maintenance and Corrective maintenance.

Dynamic grouping maintenance optimization by considering the probabilistic remaining useful life prediction of multiple equipment

For multi-equipment maintenance of modern production equipment, the economic correlation and degradation uncertainty may lead to insufficient or excessive maintenance, increasing maintenance costs. This paper proposes a dynamic grouping maintenance method based on probabilistic remaining useful life (RUL) prediction for multiple equipment. Long short term memory (LSTM) is developed to predict the equipment probability RUL by the Variational Auto-Encoder (VAE) resampling. Then, the dynamic grouping maintenance model is constructed to minimize the maintenance cost rate under the known probabilistic RUL information. The gazelle optimization algorithm (GOA) is used to determine the optimal maintenance time for each equipment. To better verify the effectiveness of the proposed method, a numerical case with six wind turbines is introduced to analyse the performance of GOA. Moreover, the advantages of dynamic grouping maintenance is verified by comparing with independent maintenance, whose maintenance cost rate is reduced by 10.01%.

Design, Simulation and Optimization of a Novel Transpired Tubular Solar Air Heater

In this paper, a novel tubular solar air heater is introduced. In this air heater, the hot boundary layer is drawn into the absorber tube and can provide thermal energy at moderate temperatures. Several different cases were simulated and a correlation was proposed to predict the collector’s effectiveness as a function Rayleigh number and Reynolds number. An equation was derived to find the effectiveness of this collector. Finally, a real case was studied with non-uniform solar flux distribution, as well as radiation heat loss. Good agreement was found between the results and those derived by the proposed analytical method. For different suction values, the first-law and the second-law efficiencies were calculated. Based on the exergy analysis, exergy destruction in absorption is the dominant factor that is unavoidable in low-temperature collectors. It was shown that there is an optimum suction value at which the second-law efficiency is maximized. At the optimum point, temperature rise can reach 54 K, which is hardly possible with a flat plate collector. Based on the exergy analysis, the relation between tube wall temperature and air outlet temperature in their dimensionless forms at the optimum working condition was derived, and it was shown that effectiveness at the optimum working condition is around 0.5. This means that the air temperature rise shall be half of the temperature difference between collector wall and the ambient temperatures. A high outlet temperature besides the low cost of construction and maintenance are the main advantages of this air heater. With such a high temperature rise, this type of collector can increase the use of solar energy in domestic applications.

A hybrid neural network based on variational mode decomposition denoising for predicting state-of-health of lithium-ion batteries

Accurately predicting the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, and reducing maintenance and service costs for associated equipment. Nevertheless, the aging data of lithium-ion batteries displays pronounced nonlinearity and is plagued by issues such as capacity regeneration. To address this issue, this study proposes a framework for SOH prediction of lithium-ion batteries based on Variational Mode Decomposition (VMD) and CNN-Transformer. First, the original data undergoes a VMD smoothing process to eliminate capacity regeneration and a portion of the noise signals. Subsequently, Convolutional Neural Networks (CNN) is utilized for feature extraction. Then, a modified Transformer model is employed to capture the inherent correlations in the time series and map the features to future SOH values. An iterative strategy is adopted to predict SOH for each charge-discharge cycle. The experimental results on the CALCE dataset demonstrate that the proposed method can accurately predict the SOH of lithium-ion batteries using just 5 %–6 % of the complete cycle's aging data. Additionally, comparative results on the NASA dataset show that, compared to the latest relevant literature, the proposed method achieves high prediction accuracy while maintaining exceptional generalization.

Efficiency and productivity gains of robotic surgery: The case of the English National Health Service.

This paper examines the effect of new medical technology (robotic surgery) on efficiency gains and productivity changes for surgical treatment in patients with prostate cancer from the perspective of a public health sector organization. In particular, we consider three interrelated surgical technologies within the English National Health System: robotic, laparoscopic and open radical prostatectomy. Robotic and laparoscopic techniques are minimally invasive procedures with similar clinical benefits. While the clinical benefits in adopting robotic surgery over laparoscopic intervention are unproven, it requires a high initial investment cost and carries high on-going maintenance costs. Using data from Hospital Episode Statistics for the period 2000-2018, we observe growing volumes of prostatectomies over time, mostly driven by an increase in robotic-assisted surgeries, and further analyze whether hospital providers that adopted a robot see improved measures of throughput. We then quantify changes in total factor and labor productivity arising from the use of this technology. We examine the impact of robotic adoption on efficiency gains employing a staggered difference-in-difference estimator and find evidence of a 50% reduction in length of stay (LoS), 49% decrease in post-LoS and 44% and 46% decrease in postoperative visits after 1year and 2years, respectively. Productivity analysis shows the growth in radical prostatectomy volume is sustained with a relatively stable number of urology surgeons. The robotic technique increases total production at the hospital level between 21% and 26%, coupled with a 29% improvement in labor productivity. These benefits lend some, but not overwhelming support for the large-scale hospital investments in such costly technology.

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.