Maintenance Scenarios Research Articles

Influence of vegetation maintenance on flow and mixing: case study comparing fully cut with high-coverage conditions

Abstract. In temperate climates, agricultural ditches are generally bounded by seasonal vegetation, which affects the hydrodynamics and mixing processes within the channel and acts as a buffer strip to reduce a load of pollutants coming from the surrounding cultivated fields. However, even if the control of such vegetation represents a key strategy to support sediment and nutrient management, the studies that investigated the effect of different vegetation maintenance scenarios or vegetation coverage on the flow and mixing dynamics at the reach scale are very limited. To overcome these limitations and provide additional insights into the involved processes, tracer tests were conducted in an agricultural ditch roughly 500 m long close to Warsaw in Poland, focusing on two different vegetation scenarios: highly vegetated and fully cut. Under the highly vegetated scenario, sub-reaches differing in surficial vegetation coverage are analysed separately to better understand the influence of the vegetation conditions on the flow and mixing parameters. Special attention has been paid to the longitudinal dispersion coefficient in complex natural conditions and its dependency on vegetation coverage (V). The vegetation maintenance decreased the travel and residence times of the solute by 3–5 times, moderately increasing the peak concentrations. We found that the dispersion coefficient decreased approximately linearly with the increase of vegetation coverage at V>68 %. Further research is needed at lower vegetation coverage values and different spatial plant distributions. The obtained longitudinal dispersion coefficient values complement dispersion value datasets previously published in the literature, which are barely available for small natural streams. The new process understanding supports the design of future investigations with more environmentally sound vegetation maintenance scenarios.

Robust mixed-model assembly line balancing and sequencing problem considering preventive maintenance scenarios with interval processing times

Robust mixed-model assembly line balancing and sequencing problem considering preventive maintenance scenarios with interval processing times

Economic Feasibility Analysis of Local Road Maintenance in West Bandung Regency: A Case Study of Cangkorah Street STA 0+000 – 1+300

This study aims to determine the economic feasibility of local road maintenance scenario in West Bandung Regency with case study of the Cangkorah Street. After knowing the International Roughness Index (IRI) value based on the IKP value from the results of a direct survey using IKP guidelines, the economic feasibility analysis is carried out based on a road maintenance scenario, while the maintenance scenario being tested is a do-something scenario, by doing road maintenance in the form of overlay once every 5 years in 10 year time compared by a do-nothing scenario that is not doing any maintenance for the next 10 years. The research instruments used were prediction of the performance of the Cangkorah Street Pavement for the next 10 years, determine road maintenance scenarios, maintenance budget plans, calculate Vehicle Operational Costs (VOC) for each scenario, and time value. The results of this study are: (1) The budget plan for the maintenance of the Cangkorah Street in the do-something scenario with overlays 3 times in 10 years is Rp. 4,168,019,982, (2) The actual VOC for the do-nothing scenario was Rp. 57,345,718,978 and the actual VOC for the do-something scenario was Rp. 3,132,189,189 with VOC savings was Rp. 54,213,529,789, (3) The time value of the scenario for the next 10 years obtained savings of Rp. 399,241,414, (4) The feasibility of do-something scenario from an economic point of view shows that the scenario is feasible with a NPV of Rp. 36,674,480,627>0 and a BCR of 11,39 >1.

Assessing the usefulness of a visual programming IDE for large-scale automation software

Industrial control applications are usually designed by domain experts instead of software engineers. These experts frequently use visual programming languages based on standards such as IEC 61131-3 and IEC 61499. The standards apply model-based engineering concepts to abstract from hardware and low-level communication. Developing industrial control software is challenging due to the fact that control systems are usually unique and need to be maintained for many years. The arising challenges, together with the growing complexity of control software, require very usable model-based development environments for visual programming languages. However, so far only little empirical research exists on the practical usefulness of such environments, i.e., their usability and utility. In this paper, we discuss common control software maintenance tasks and tool capabilities based on existing research and show the realization of these capabilities in the 4diac IDE. We performed a walkthrough of the demonstrated capabilities using the cognitive dimensions of notations framework from the field of human–computer interaction. We then improved the tool and conducted a user study involving ten industrial automation engineers, who used the 4diac IDE in a realistic control software maintenance scenario. Based on lessons learnt from this study, we adapted the 4diac IDE to better handle large graphical models. We evaluated these changes in a reassessment study with automation engineers from seven industrial enterprises. We derive general implications with respect to large-scale applications for developers of IDEs that we deem applicable in the context of (visual) model-based engineering tools.

A Comparative Study of Optimization Models for Condition-Based Maintenance Scheduling of an Aircraft Fleet

Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of resource-constrained scheduling problems under uncertainty and is often solved using a mixed integer linear programming (MILP) formulation. While a MILP framework is very promising, the problem size can scale exponentially with the number of considered aircraft and considered tasks, leading to significantly high computational costs. The most recent advances in artificial intelligence have demonstrated the capability of deep reinforcement learning (DRL) algorithms to alleviate this curse of dimensionality, as once the DRL agent is trained, it can achieve real-time optimization of the maintenance schedule. However, there is no guarantee of optimality. These comparative merits of a MILP and a DRL formulation for the aircraft fleet maintenance scheduling problem have not been discussed in the literature. This study is a response to this research gap. We conduct a comparison of a MILP and a DRL scheduling model, which are used to derive the optimal maintenance schedule for various maintenance scenarios for aircraft fleets of different sizes in a disruptive environment, while considering health prognostics and the available resources for the execution of each task. The quality of solutions is evaluated on the basis of four planning objectives, defined according to real airline practice. The results show that the DRL approach achieves better results with respect to scheduling of prognostics-driven tasks and requires less computational time, whereas the MILP model produces more stable maintenance schedules and induces less maintenance ground time. Overall, the comparison provides valuable insights for the integration of health prognostics in airline maintenance practice.

Potential and Improvement of Maintenance Efficiency of Agricultural PTO Shafts by a New Digital Maintenance Assistant

Power take-off (PTO) shafts are widely used in agricultural machinery to directly power implements with the tractor’s engine. While the operating conditions such as dirt, weather, peak loads and utilization greatly influence a PTO shaft’s lifetime, proper maintenance, especially lubrication, is necessary for an appropriate operation and maximum lifetime. Hence, this paper evaluates the potential for saving resources during maintenance by using a digital maintenance assistant. To simulate and study the cost of PTO shafts for agricultural implements, an investigation of the lubrication process of PTO shafts was carried out on several test farms. In practice, two extremes are common: Either far too much grease is used for best possible lubrication, or the PTO shaft is not lubricated at all. To simulate the annual cost of maintenance, three scenarios were calculated and compared: “Maintenance before usage” and “no maintenance” as extreme scenarios and “maintenance according to the manufacturer’s specifications” by a digital maintenance assistant as an optimum scenario. The simulation of different maintenance scenarios shows a reduction in maintenance costs of up to 93.8%, an average reduction of lubricants by 86.3% and an average labor time reduction by 81.8% for the case of “maintenance according to the manufacturer’s specifications”. The results of this work show that there is potential for major savings using a digital maintenance assistant. This development could be the first step towards the digitalization of maintenance activities on agricultural farms.

Pavement management system: implementing project evaluation for local road administrations

Pavement Management System (PMS) is widely used by highway and airport administrations for the rational managing of pavement networks and for identifying cost-effective maintenance strategies. Local Road Administrations (LRA) need the implementation of PMS due to the limited financial, technology and specialised human resources. To lead the LRA to a more conscious and effective use of PMS, tailored procedures and tools have to be defined considering their own potentiality and requirements. For this purpose, the Geographic Information System (GIS) can be implemented, which facilitates the collection, management and analysis of road inventory and monitoring data. GIS allows to easily display on a thematic map a wide set of geographically referenced information and to relate them with spatial and data management tools. Furthermore, customised GIS-based tools can be created to enhance pavement management and to assist the decision-making phase.This paper shows how the implementation of the project evaluation phase in a tailored PMS for several local road administrations in the center of Italy, in order to support maintenance planning and to identify the effective maintenance strategies in a long-term perspective. The process is based upon a systematically and progressive application of interconnected activities, which are supported by reference documentations and specific GIS tools. Specific GIS-based tools were developed for the set-up of road database, prioritisation, selection of properly repairs and maintenance planning. To achieve a more rational and objective maintenance planning assessments, alternative maintenance scenarios were defined using predictive models to estimate the evolution of pavement conditions over time. Long-term effectiveness of alternative maintenance strategies was established using a customised strategic index (SI).

Design and Development of Aviation Aircraft Maintenance Training Platform Based on VR Technology

The maintenance process of aviation aircraft is a complex and high-risk task. This article aims to design an aviation aircraft maintenance training platform based on Virtual Reality (VR) technology to improve the training efficiency and quality of aviation aircraft maintenance personnel. By analyzing the maintenance process of aviation aircraft and combining VR technology, an interactive training platform is designed that can simulate real maintenance scenarios and provide a comprehensive training experience. The research results indicate that in terms of operational efficiency, the on time completion rate of maintenance implementation for aviation aircraft maintenance training platforms designed using VR technology has increased from 79% before optimization to 94%. The aviation aircraft maintenance training platform based on VR technology has high effectiveness and practicality, which can effectively improve the training efficiency and quality of aviation aircraft maintenance personnel.

The gamma dose rate analysis method for the maintenance of China initiative accelerator driven subcritical system based on the voxelized algorithm

Gamma dose rate analysis is the important issue for the maintenance of China initiative accelerator driven subcritical system (CiADS), which has direct impact on the nuclear radiation protection and the safe working environment for operators. However, the gamma radiation assessment in CiADS facility is the complex and challenge task, since there are a lot of complicated geometries with large amount of high order curve surfaces that are difficult to describe in the simulation process. Additionally, the shielding design in maintenance scenario has the requirement of high efficient calculation with acceptable accuracy. In this context, the point kernel method and the model simplification procedure by means of the voxelized model description have been adopted in the dose rate analysis for the purpose of obtaining the required flexible and diverse features, where the complex three dimensional geometries constructed by computer aided design (CAD) tools can be automatically converted to the approximate voxelized structure, and the radiation sources with arbitrary shapes are sampled based on the basic units in the conversion model. Furthermore, the CAD geometry optimization method has been verified in considering the volume deviation between the original CAD structure and the voxelized model. The feasibility and applicability of the dose rate analysis method have been tested in comparison with the FLUKA code, where all the numerical simulation results demonstrate that the proposed assessment method has the potential in meeting the design requirements during the maintenance of CiADS facility.

Blockchain Enabled Maintenance Management Framework for Military Equipment

Maintenance of military equipment plays a vital role in achieving higher war readiness. The vast number of military equipment and their deployment on distinct locations make their maintenance a challenging task. Decision-makers are always interested in knowing the latest status of equipment, develop effective maintenance strategies, and predict mission reliability as accurately as possible. To suffice this need, maintenance data of military equipment must be maintained with high granularity and accuracy, including maintenance information about every lowest maintainable unit of every military equipment. There is a profound need for a comprehensive maintenance management framework, which addresses several military-specific issues with equipment maintenance and stores the maintenance data in the utmost secure environment. In this article, we attempt to provide a novel blockchain enabled framework to make military maintenance management comprehensive and future ready. This article first provides a representation of the maintenance scenario of military equipment and later discusses the several choices made available within the blockchain technology, along with the architecture of the proposed blockchain. The proposed framework uses smart contracts to make monitoring and validation more uncompromising and with minimum human intervention. Some pragmatic analytics are presented in this article, along with the inherent blockchain advantages.

Investigating thresholding techniques in a real predictive maintenance scenario

We deal with a real predictive maintenance (PdM) scenario in an Industry 4.0 setting. With a help of the Sibyl platform, we can monitor live data from key components of a Philips factory equipment; in this work, we focus on a cold-forming press. Due to the dynamic environment of the operation of this press, unsupervised anomaly detection techniques are used to timely detect the wear, where early anomalies are interpreted as warning signs of a forthcoming failure. Typically such techniques assign an anomaly score, and the problem we face is how to appropriately set a threshold for this score. We introduce and compare four generally applicable thresholding techniques, two of which are dynamic, i.e., they continuously refine the threshold during the episode lifetime. We discuss the properties of these techniques and quantitatively evaluate their behavior in our case study.

Balancing and sequencing of mixed-model assembly line considering preventive maintenance scenarios: mathematical model and a migrating birds optimization algorithm

Balancing and sequencing of mixed-model assembly line considering preventive maintenance scenarios: mathematical model and a migrating birds optimization algorithm

Railway Bridge Management System Based on Visual Inspections with Semi-Markov Continuous Time Process

This article describes a model for predicting the degradation of in-service railway bridges based on a semi-Markov continuous time process. This model relies on the history of inspections of 588 bridges located on a heavy-haul railway line in Brazil, between 2016 and 2020. A dedicated computational tool developed in Matlab allows the automated data processing. A parametric study is performed to understand which factors derived from the bridge structural characteristics, as well as operational and environmental factors, most influence the deterioration model. The type of material proves to be a decisive factor and therefore two specific prediction models are stablished, one for concrete bridges and other to steel bridges. The prediction models have an efficiency equal to 93.7%, for concrete bridges, and 95.1% for steel bridges. Additionally, the analysis of several preventive and corrective maintenance scenarios, specifically for concrete bridges, allows to optimize the condition ratings during the life cycle.

ANALYTICAL PREDICTION ON AGING DETERIORATION BY USING WHOLE TRUSS BRIDGE MODEL CONSIDERING LOCAL CORROSION PROGRESS

In Japan, drawing reasonable maintenance scenarios are required because increase in aging bridges is becoming more serious problem. For implementing maintenance scenarios, it will be effective to predict the deterioration of remaining load bearing capacity of structural members or whole bridge considering with corrosion damages in steel bridges. In this study, numerical corrosion surfaces which assumed until 120 years after were generated intending for actual aging truss bridge (about 100 years old) with many local corrosion damages. Each local corrosion surfaces were generated by using a simple corrosion progress model depending on probability distribution. Then, finite element analyses of this truss bridge which has numerical local corrosion surfaces were carried out for clarifying remaining load bearing capacity. Ultimate load bearing capacity was estimated from the maximum live-load magnification (αmax) based on load-rating method in whole bridge analyses. Also, the deterioration curve of remaining load bearing capacity on whole bridge can be obtained as the relationship between the max and elapsed years (t). From the analytical results, the maximum load bearing capacity of the initial state model was estimated to αmax=2.98 if the L live load in JSHB was assumed. And remaining load bearing capacity of whole bridge assuming after 120 years was estimated to αmax =2.35 from αmax-t relation. It will mean that the load bearing capacity has been decreased 21.1% than initial state.

NUMERICAL GENERATION OF LOCAL CORROSION SURFACE USING CORROSION PROGRESS MODEL FOR STRENGTH PREDICTION

In aging steel bridges, it is necessary to accurately evaluate Life Cycle Cost (LCC) by judging more suitable timing for repair and reinforcements in order to keep good condition in service for a long period of time. Therefore, it will be important to predict as accurately as possible the remaining strength reduction in future for drawing reasonable maintenance scenario in aging steel structures. Since local corrosion damage often appears depending on structural details in members, it will be thought that corrosion progress according to corrosion environment and periodic inspection results must be considered to remaining strength estimation. In this paper, the remaining bending analyses on corroded H-shape weathering steel which had been used for 20 years, were conducted for future prediction of remaining strength reduction by using local corrosion surface data generated numerically. These surface data were generated by applying a simple generation model based on past corrosion progress model. Moreover, this model can consider locational concentration of attack factors by adopting appropriate probability distribution referred the contour maps of remaining thickness for actual local corrosion surfaces. From the analytical results, both remaining strength and torsional buckling behavior of upper flange with artificial corroded surface were almost the same to experimental results. Also, it will be predicted that remaining strength after 30 years from now was decreased as about 38% than initial state.

Remote maintenance scenario of the beam line components of the ITER neutral beam system

The ITER neutral beam (NB) cell contains the neutral beam injectors (NBI) that provide heating power for fusion reaction in the tokamak machine. The NB cell is a radiation-controlled area where human worker access is strictly limited or prohibited depending on the NB cell configurations. Establishing the maintenance processes in the early design phase of a RH system is crucial in order to guarantee that the overall system configuration satisfies the maintenance requirements in given conditions and constraints. This paper describes the remote maintenance scenario of the beam line components (BLC) of the NB system by using the neutral beam remote handling system (NBRHS). This operation is a corrective maintenance operation that could happen multiple times during lifetime of ITER. If one of the BLCs fails, the failed NBI is isolated by closing the absolute valve to continue the tokamak operation. After that when the long-term maintenance campaign starts, the failed BLC needs to be replaced. The maintenance sequences involve human worker hands-on operations in the hot cell complex (HCC) and in the NB cell to prepare the RH operations, deployment operations of the NBRHS and the RH tools, removal/opening/transportation/storage operations to access to the failed component, and to remove and replace the failed component. Many RH tools are required during these processes such as unbolting/bolting, connector disconnection/connection, pipe cutting/welding/inspection, heavy and large component handling etc. Moreover, the operations need to be done without or minimizing spreading of contamination from the NBI vessels/components to the NB cell as the NB cell shall remain human accessible area during the long-term maintenance periods. The maintenance sequence study shows that the developed RH sequence is applicable in the current NB cell environment and is compatible with the defined interfaces in the plant systems. The developed scenario needs to be elaborated further detail in the next design phases together with the detail design of the RH equipment and the RH tools.

Assessment of video see-through smart glasses for augmented reality to support technicians during milking machine maintenance

Smart glasses for augmented reality are digital technology under investigation in the agricultural sector. The potential of augmented reality was underlined, in some scientific contributions, as a support tool for farmers' activities and for the decision-making process. One of the most practical applications studied for augmented reality was in maintenance operations, where the use of smart glasses showed high capability. This work focuses on the evaluation of the performance and applicability of smart glasses with a video see-through display system and testing the device's available functions in agricultural frameworks. In addition, an augmented assistance scenario describing the main steps involved in the functioning of the maintenance operation was developed for milking machine inspection. The audio–video quality, battery life, detection capabilities of markers, and voice control interaction system were evaluated. The results showed the capabilities of smart glasses to reach augmented information from a long distance in a short time interval and to transmit audio and video with a high level of detail, allowing discrimination of small objects during remote assistance with reduced delay. The built maintenance scenario represents an example of augmented reality digital assistance application in the inspection and maintenance of the milking machine. The device performance and the proposed maintenance scenario underline the potential that augmented reality could have in the agricultural sector to assist and guide both farmers and technicians to timely problem solving. This solution fits into the agriculture 4.0 perspective, which is increasingly focused on digital transformation to improve farms’ efficiency and sustainability.

Robust assembly line balancing problem considering preventive maintenance scenarios with interval processing time

Considering preventive maintenance (PM) scenarios in the assembly line balancing problem (ALBP) have been proved effective and can be used to increase production efficiency. All these studies assumed that the processing time of tasks was fixed. However, many factors can affect the processing time in the actual production process and make the original task allocation plans infeasible. The robust method is introduced to solve the uncertain processing time, and the robust ALBP considering PM scenarios (RALBP_PMs) is studied in this paper. In this problem, three objectives are optimized: the cycle time under the regular scenario, the maximum cycle time, and the maximum task alteration under all PM scenarios. A robust mixed-integer optimization model is proposed to hedge against uncertainty, and then it is linearized by duality to make it solvable. A Q-learning-based variable neighborhood search (QVNS) algorithm with several improvements is designed to solve this problem. Several heuristic rules are designed to improve the performance of the initial population. Roulette wheel selection based on crowding distance is designed to select a solution with more potential. Eleven problem-specific neighborhood structures are designed to improve search efficiency. These structures are sorted by the Q-learning-based sorting method to find a good solution quickly. Experiment results demonstrate that considering PM scenarios in RALBP is necessary. The proposed mathematical model can obtain the Pareto solutions of small-scale instances, and the proposed QVNS algorithm is more suitable for solving large-scale instances.

Collaborating product development and maintenance service: quality incentive with inspection and warranty strategies

ABSTRACT Service-oriented manufacturing has been a prevalent trend in integrating product collaborative development with maintenance service, whereas the final products are frequently trapped into severe quality defects lacking appropriate contracting incentives. This paper establishes a tractable framework investigating the alignment of inspection and warranty strategies to manage product quality throughout the R&D, manufacturing and usage processes. Specifically, the manufacturing effort is motivated by a cost-sharing contract provided by the researcher, who performs quality inspection upon receiving the final product. Subsequent maintenance service is predesignated for warranty period coverage that the cost is partially claimed for compensation from the manufacturer. Under both breakdown and preventive maintenance strategies, we elaborate and compare the optimal incentive intensities solving the equilibrium effort decisions and inspection accuracy. The results suggest that both R&D and manufacturing effort levels under preventive maintenance are slightly higher than that under breakdown maintenance scenario, while inspection accuracy contradicts conversely. The breakdown maintenance strategy is associated with a lower proportion of warranty costs borne by the manufacturer. For a sufficient long warranty period, the researcher is better off to conduct preventive maintenance periodically. Our study contributes to providing explicit implications to manage product development quality with adequate inspection and warranty strategies.

Assessment of vehicular fuel consumption and interaction with pavement characteristics using HDM-4 on Indian urban road network: A case of Pune city

Rapid urbanization and exponential increase in the number of vehicles on city roads have triggered issues like air pollution and accidents, endangering the health of millions of people in Indian cities. Vehicular emission is a major contributor to air pollution in Indian cities. This study primarily analyzes the relative effects of riding quality and pavement surface type on vehicular fuel consumption (FC) in an urban environment. This study utilizes the Road Asset Management System (RAMS) applications to predict FC in different scenarios of pavement maintenance. Highway Development and Management (HDM-4) software was integrated into RAMS for network & economic analysis and FC prediction of the road network. The study was conducted on the Pune city road network in India, and the results revealed a 2.32 % reduction in fuel use if timely maintenance is provided. Over 0.128 million liters of fuel per 1000 vehicle-km can be saved over the study network of only 36.5 km over the next 15 years, equivalent to US$ 155,206.18 in 2021. The analysis of covariance test results showed the significant effect of pavement surface type on fuel consumption, with 4.86 % less fuel consumed on cement concrete surfaces than bituminous surfaces. About 99.431 liters per 1000 vehicle-km of fuel were utilized by the vehicle fleet on concrete pavements, while 104.513 liters per 1000 vehicle-km of fuel were consumed on bituminous pavements after adjusting for roughness characteristics of both the surface types.