Maintenance Methods Research Articles

Ring Magnet-Guided Magnetic Manipulation for Biofabrication of 3D Cellular Structures.

Negative magnetophoresis is employed to levitate cells in a paramagnetic medium without the need for magnetic labeling, preserving their natural state and minimizing toxicity. The single-ring magnet configuration that provides an open space in the levitation chamber enhances culture accessibility and scalability, enabling the formation of millimeter-sized 3D structures through cellular self-assembly. This system provides a versatile and cost-effective approach for diverse applications, including tissue engineering and biofabrication. This protocol outlines a method for biofabrication and maintenance of 3D cellular structures using magnetic levitation with a ring magnet-based setup.

Improved quantum particle swarm-based collaborative optimisation and control method for flexible job shop scheduling and predictive maintenance

Improved quantum particle swarm-based collaborative optimisation and control method for flexible job shop scheduling and predictive maintenance

Methods for Thrips Transmission and Maintenance of Tomato Spotted Wilt Virus (TSWV).

Tomato spotted wilt virus (TSWV) is transmitted by insect pests from the Thripidae family, including Frankliniella occidentalis, commonly known as western flower thrips. For experimental purposes, researchers have developed methods for inoculating host plants with TSWV, allowing thrips to acquire TSWV, and verifying thrips acquisition. Plants can be inoculated with TSWV either mechanically or with thrips in the lab, but in nature, the virus is transmitted by thrips. For the study of tospovirus transmission biology and vector-virus interactions, efficient methods for mechanical and thrips transmission are essential. We have optimized these protocols for TSWV and western flower thrips, but they can also be adapted to other tospovirus-vector systems.

Quantification of lifecycle costs for porous asphalt life-extension maintenance methods under managerial uncertainties

ABSTRACT The objective of this research was to evaluate the lifecycle costs associated with emerging pavement maintenance technologies, namely, in-situ rejuvenation and very open emulsion asphalt concrete (ZOEAB+), and scrutinise their suitability over corrective resurfacing maintenance using a stochastic approach. A rational lifecycle inventory was developed by conducting interviews and questionnaire surveys with experts and referring to standard guidelines and international databases. The net present value (NPV) was found sensitive to 12 different inputs with traffic growth rate and discount rate causing the highest uncertainty followed by gasoline and diesel prices. Monte Carlo simulations suggested that the median uncertainty in NPV by using in-situ rejuvenation and ZOEAB+ was 13% and 4% lower than resurfacing. It is envisioned that the research outcomes will assist decision-makers in understanding the uncertainties and costs associated with different maintenance alternatives in the early stages of the project to foster procurement of sustainable and circular pavement maintenance strategies.

Strategic asset management in LNG Plants: A holistic approach to long-term planning, rejuvenation, and sustainability

The liquefied natural gas (LNG) industry plays a vital role in the global energy landscape, balancing growing energy demands with the imperative of reducing environmental impacts. However, the sector faces significant challenges, including aging infrastructure, the need for operational efficiency, and adherence to sustainability goals. This paper explores the role of strategic asset management (SAM) as a holistic solution for optimizing LNG plant operations. It introduces a comprehensive framework that integrates organizational, financial, and technical strategies, emphasizing long-term planning, rejuvenation, and sustainability. Key strategies, such as predictive maintenance, digital twins, and risk-based inspection methods, are discussed for their effectiveness in extending asset lifecycles and minimizing downtime. Furthermore, the paper underscores the importance of environmental, social, and governance (ESG) factors, renewable energy integration, and resilience planning in addressing climate risks and market volatility. Concluding with actionable recommendations, this paper provides a roadmap for stakeholders to adopt SAM practices, ensuring sustainable, resilient, and competitive LNG operations. Keywords: Liquefied Natural Gas, Strategic Asset Management, Sustainability, Predictive Maintenance, Digital Twin Technology, Resilience Planning.

Effect of bone marrow niche on hematopoietic stem cells.

Hematopoietic stem cells (HSCs) reside in a milieu that supports their functions, differentiation, and survival. This niche consists of several types of cells, including mesenchymal stem/stromal cells, endothelial cells, osteoblasts, megakaryocytes, macrophages, adipocytes, lymphoid cells, and nerve fibers. The interactions between these cells and HSCs have a role in HSC fate. Several studies have focused on identification of the biological and cellular mechanisms contributing to the establishment of this niche. However, the exact mechanisms of the interaction between HSCs and the bone marrow niche have not been elucidated yet. Unraveling these mechanisms would help in the design of effective methods for maintenance and multiplication of HSCs in clinical settings, in addition to establishment of novel therapies for hematopoietic diseases. The current review summarizes the effects of the niche cells on HSC function and underlying mechanisms of interplay between HSCs and their niche.

Life cycle cost analysis of ultra-thin white topping of geopolymer concrete overlay on bituminous concrete pavement

The social and economic growth of nations greatly depends on effective pavement maintenance methods that adhere to sustainable principles, ensuring long-term durability and performance. Conventional methods for maintaining bituminous concrete pavements, such as using a bituminous concrete overlay, are not always the most efficient. In contrast, concrete overlays have consistently demonstrated superior performance globally. This paper focuses on the design, analysis of life cycle costs, and assessment of emissions of greenhouse gases associated with bituminous concrete pavement from a life cycle perspective. It evaluates two maintenance strategies: a bituminous concrete overlay and an ultra-thin geopolymer concrete overlay (often called "white topping"). The findings suggest that utilizing sustainable materials in an ultra- thin white topping (UTWT) geopolymer concrete overlay is a more favorable option when compared to a bituminous concrete overlay. The life cycle cost study, utilizing the net present value approach, demonstrates its superior economic viability in the long run. The study suggests that using a very thin layer of white topping geopolymer concrete as an overlay on bituminous concrete pavement improves its long term performance and provides economic and environmental advantages.

RELATIONSHIP OF THE CULLING OF FIRST-BORN COWS FROM THE HEARD WITH THE SCORE OF LINEAR TYPE TRAITS UNDER VARIOUS HOUSING

The results obtained in breeding plants indicate the influence of the technology of housing primiparous cows on their score of linear type traits. The influence of this factor ranged from 0.4% (p<0.01) to 2.9% (p<0.001). It was found that when stall housing, animals with a high assessment of body depth, angularity, and rear legs had a lower percentage of abandonment from the herd compared to animals with an average and low assessment of these traits (p<0.05). The results obtained when determining the percentage of abandonment of cows with different score of fore udder attachment indicate a greater staybility of first-born cows with a high assessment of this trait, which was 7.5-11% higher compared to animals with a lower score. The assessment of the central ligament of the udder (1.3%; p<0.05) and the depth of the udder (1.3%; p<0.05) had a probable impact on the abandonment of first-born cows from the herd. The number of animals that left the herd during the study period with a low assessment of the central udder ligament was higher by 0.7-13% compared to animals with a high and average assessment. A similar relationship between the loss of animals was established with the assessment of the depth of the udder (by 8.8-17.1%). The culling of first-born cows depending on the assessment of body structure traits in free housing, according to our data, probably depended only on one factor – the slope of the sacrum. The strength of the impact of the assessment of this trait was 2.9%. It is important to note that the percentage of animals leaving the herd with an average assessment of the slope of the sacrum was the lowest. When comparing the trends in the influence of the body structure traits of first-born cows on the level of their loss from herds under different methods of maintenance, it was found that, unlike tied housing, under free housing, there was a tendency to increase the percentage of culling first-born cows with a high growth assessment. Also, cows with a wide chest and a deep body had the highest risk of culling compared to the group of animals with average linear score indicators. There was a slight decrease in the percentage of culling of first-born cows with an increase in the score for the dairy type in both housing methods, animals with the desired posture of the hind limbs and a high score of the central ligament of the udder and body condition.

Modern methods of traction maintenance of river freight trains

The article considers modern methods of traction maintenance of freight trains in river transport. The main attention is paid to the operation of river heavy-duty trains. The study is based on information on the movement of the fleet in the Volga and Kama basins. Based on the results of the analysis, the forms of traction maintenance of tonnage used in practice are presented. To assess the interaction of pushers and non-self-propelled vessels, quantitative parameters were used that characterize the degree of traction assignment to the tonnage and the frequency of change of the barges of the train during operation. Factors limiting the possibility of shipping companies to choose rational schemes for traction service of river trains in modern conditions are identified.

Key structure and technology of bridge cable maintenance robot – a review

Purpose The cable inspection robot is essential in maintaining bridge cables. The purpose of this paper is to summarize the maintenance methods of bridge cables. It summarizes the advantages and disadvantages of the critical structures of the external overall frame, intermediate adhesion device, attachment mechanism and driving method of the cable inspection robot. Finally, it discusses the challenges the cable inspection robot faces and the direction of future research. Design/methodology/approach This paper summarizes the research progress of the cable inspection robot and details the advantages and disadvantages of critical structures such as the external frame, intermediate adhesion device, attachment mechanism, driving method and safe return device of the robot. Finally, it points out the future direction of cable inspection robots, including lightweight design, hybrid design, multi-robot cooperative work, multi-technology integration and intelligent cable inspection digital twin model. Findings The cables are the main load-bearing components of a bridge, and their safety is crucial. However, subjected to varying loads and environmental influences over a long period, cables are prone to damage, threatening the bridge’s stability. Cable inspection robots can comprehensively detect and repair cable damage, significantly improving efficiency and safety. Originality/value This paper provides a comprehensive review of the current research on cable inspection robots, enabling readers to have a comprehensive and systematic understanding of the critical structures and key technologies of cable inspection robots and providing scientific references for researchers working on cable inspection robots.

Factors Associated with Wash Behavior Using Theory Of Planned Behaviour Among Adolescents in Islamic Boarding School

Background: The behavior of Handwashing with Soap (HWWS) is a method of hand hygiene maintenance that protects oneself and those around from diseases. However, the habit of washing hands with soap might not be a routine practice among adolescent students in maintaining daily personal hygiene. The purpose of this study was to analyze the factors related to HWWS behavior among the students of Islamic boarding school based on the Theory of Planned Behavior (TPB). Method: This research was an observational study with a cross-sectional design. The sample consists of 104 students, aged 10 to 21 years old (53 male and 51 female) selected purposively. The data analysis conducted to assess the strength of relationships between four elements in the TPB and HWWS behavior as well using the contingency coefficient and Spearman correlation. Result: The results of the study show that behavioral intention was strongly associated with HWWS behavior (C = 0.609, p < 0.001). Meanwhile, the four TPB elements had moderate level of associations (C = 0.484 – 0.594, p < 0.001). To conclude, efforts are needed to improve HWWS facilities and strengthen HWWS behavior through programs tailored to the students' characteristics and needs.

Research on the Stress Characteristics of Reuse of Semi-Rigid Base.

Semi-rigid bases are widely used in road construction due to their excellent properties, high rigidity, and frost resistance, and they have been in service for many years. However, as the service life increases, the maintenance demands also grow, with traditional maintenance methods still being the primary approach. Based on a typical case using ground-penetrating radar (GPR) technology, this study explores the issue of cracks in semi-rigid bases and their impact on overlay layers. The findings indicate that the overlay layer at semi-rigid base cracks struggles to withstand significant tensile and shear stresses, leading to reflective cracking and reducing pavement durability. To address this problem, this paper investigates the application potential of crushing technology in maintaining semi-rigid bases. Crushing technology has been widely employed in the maintenance of cement concrete panels, effectively eliminating reflective cracks and extending the service life of overlays. Given that semi-rigid bases share similar high-strength characteristics with cement concrete panels, crushing technology shows considerable applicability in semi-rigid base maintenance. This study employs a finite element analysis method to establish a semi-rigid base model under the impact load of multi-hammer equipment. It examines its dynamic mechanical response and evaluates the feasibility and effectiveness of crushing technology for semi-rigid base maintenance. Additionally, this study investigates the influence of the crushed layer's modulus and thickness on key mechanical design indicators of the overlay and proposes recommendations for optimal design parameters. The research results provide valuable references for the design of the thickness and modulus in maintaining and repairing semi-rigid bases, contributing to improving pavement performance and durability.

Artificial intelligence-driven predictive maintenance in IoT systems

The study looks at the application of AI-driven predictive maintenance in IoT systems. Predictive device failure, efficient reduction in system downtime, reduced maintenance costs, and overall efficiency in connected devices will be enabled through machine learning and deep learning algorithms. The AI models developed within this research were able to provide a prediction accuracy of 92%, while the traditional methods of maintenance were far behind at 78%. It resulted in a 35% reduction in system downtime and a 28% decrease in maintenance costs while reducing the error rate to 8%. The above results bring out the potential of AI-based solutions for real-time predictive maintenance over complex IoT networks. It concludes by indicating some further research vectors, such as the refinement of the model and the extension of AI-driven predictive maintenance for broader applications in IoT, such as smart cities and healthcare systems.

Short-Term Water Supply Forecasting for Water Treatment Plant Using Temporal Multi-Scale Features

Forecasting the water supply of a water treatment plant is an important management and decision-making task in the water treatment system. Currently, most studies in this field do not consider the problems brought about by the time scale when selecting the input feature set. Therefore, this study explores how to select the relevant input feature set and model for the ultra-short-term water demand forecasting (UWDF) of water treatment plants from the perspective of time multi-scale features, in order to provide more accurate and effective water supply planning for water treatment plants. This study uses feature engineering to analyze the importance of relevant features affecting water supply, and compares and analyzes the predictive capabilities of seven deep learning models. The experimental results show that the time multi-scale features (TMFs) extracted from the historical water supply data play an important role in the prediction of ultra-short-term water supply, and the LSTM model performs better in the prediction task, with a mean absolute percentage error (MAPE) of 5.3530% and a goodness-of-fit (R2) of 0.9981. This study provides an effective method for intelligent operation and maintenance in the field of water treatment, and has broad application prospects.

Modelling the Prioritisation of Technical Objects Using the EPN Indicator

The objective of this article is to analyse and evaluate the effectiveness of predictive maintenance for machines performing key functions within a production structure. This article presents a methodology for determining the Equipment Priority Number (EPN), calculated based on parameters such as energy consumption, the criticality of machines in the value stream, and their impact on the continuity of the supply chain. The experimental implementation of a system for monitoring operational parameters—including current consumption, vibrations, and torque moments—enabled the prediction of potential failures and the planning of maintenance actions, which contributed to improving operational stability and reducing the risk of unplanned downtime. The obtained results confirm the effectiveness of the proposed methodology and demonstrate that a predictive maintenance system supported by the EPN indicator enables accurate prioritisation of maintenance activities in an actual production system. The findings also show that implementing the EPN algorithm allows for more precise prioritisation in highly customised production environments. Furthermore, the analysis of the collected data suggests the potential for further optimisation through the integration of data-driven diagnostics and artificial intelligence methods, which could enhance the efficiency and competitiveness of the system. This study’s conclusions provide a foundation for advancing predictive maintenance methods in industrial production.

Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability

This paper presents an efficient schedule method for maintenance, repair, and overhaul (MRO) tasks for aircraft engines using a constraint programming algorithm. Using data obtained from Korean Air’s MRO maintenance logs, we analyze and predict the optimal scheduling of regular inspections and fault repairs for various engine types. By proposing a proper modeling of the problem and preparing data for the constraint programming algorithm, we demonstrate superior performance in scheduling efficiency and resource utilization. The experimental results show an average utilization of 99.35%, and the method can even achieve 100% utilization in some cases.

Fabrication of a novel "all in one glove"- a functional tool for oral hygiene maintenance and the assessment of its effectiveness on plaque control in spastic cerebral palsy patients.

Spastic Cerebral Palsy (CP) is a neurological disorder from non-progressive brain injury or malformation during development, severely impairing movement, coordination, reflexes, posture, balance, and motor skills. Individuals with spastic CP experience limb spasticity, complicating tasks like precise gripping, particularly with toothbrushes. This study proposed a novel "all-in-one glove" for oral hygiene, assessing its efficacy and comparing it to conventional toothbrushes for spastic CP patients. This randomized controlled trial was conducted on 30 clinically diagnosed spastic CP patients with Group 1 consisting of 15 Subjects with clinically diagnosed spastic CP with generalized gingival diseases using conventional oral hygiene techniques with weekly oral hygiene reinforcement and Group 2 consisting of 15 Subjects with clinically diagnosed spastic cerebral palsy with generalized gingival diseases using the novel "all in one glove" method for oral hygiene maintenance with weekly oral hygiene reinforcement. The Clinical Parameters assessed includes Full mouth plaque scores, Full mouth bleeding scores and OHI scores recorded at baseline, 3 and 6 months. A statistically significant reduction was observed in Full-mouth Plaque, Bleeding, and Oral Hygiene Index (OHI) scores in both the groups (48.87% & 43.285%, 31.57% & 26.66%, 57.29% & 47.37% respectively). The Group 2 exhibited a marginally higher percentage reduction in all clinical parameters compared to the Group 1 at the measured time points. The utilization of the "All in one glove" has improved the effectiveness of oral hygiene maintenance and gingival health in spastic CP individuals. [ClinicalTrials.gov], identifier, [REF/2022/06/055641].

Low Concentration C2H2 Detection by NiO/SnO2 Nanostructured Heterojunction Based MEMS Sensor

Abstract Low concentration detection and analysis of dissolved characteristic gas Acetylene (C2H2) in oil is one of the effective methods for power transformer condition maintenance to ensure safety. The MEMS gas sensor based on the nanostructured material has become a research hotpot for detecting the C2H2. In this work, SnO2 and NiO thin films were successively deposited by RF sputtering, followed by annealing at different temperatures to prepare NiO/SnO2 heterostructures, and the C2H2 sensor chips were prepared by MEMS (microelectromechanical systems) compatible process by using the SnO2/NiO thin films as sensitive materials. The results showed that the detection limit of the MEMS sensor based on the NiO/SnO2 (NiO/SnO2-2) film annealed at 450℃ was as low as 0.2×10-6 toward C2H2. The sensor exhibits a high response at 260℃ (S=7.9 toward 1.4×10-6 C2H2), which is about 4.5 times that of pure SnO2(1.8). Meanwhile, it has an extremely short response time (13 s and 19 s). This study reveals that dissolved acetylene gas in transformer oil can be detected more efficiently by the sensor with nanostructured heterojunctions as sensitive materials.

Noise robust classification of carbide tool wear in machining mild steel using texture extraction based transfer learning approach for predictive maintenance

Acoustics based smart condition monitoring is a viable alternative to mechanical vibrations or image-capture based predictive maintenance methods. In this study, a texture analysis based transfer learning methodology was applied to classify tool wear based on the noise generated during mild steel machining. The machining acoustics were converted to spectrogram images and transfer learning was applied for their classification into high/medium/low tool wear using four pre-trained deep learning models (SqueezeNet, ResNet50, InceptionV3, GoogLeNet). Moreover, three optimizers (RMSPROP, ADAM, SGDM) were applied to each of the deep learning models to enhance classification accuracies. Primary results indicate that the InceptionV3-RMSPROP obtained the highest testing accuracy of 87.50%, followed by the SqueezeNet-RMSPROP and ResNet50-SGDM at 75.00% and 62.50% respectively. However, SqueezeNet-RMSPROP was determined to be more desirable from a practical machining quality and safety perspective, owing to its greater recall value for the highest tool wear class. The proposed acoustics-texture extraction-transfer learning approach is especially suitable for cost effective tool wear condition monitoring involving limited datasets.

Detection of Damage Motor and Coal Crusher in Power Plant Tanjung Enim 3 X 10 MW Using Vibration Analysis

Motor and coal crushers are critical in coal size reduction for efficient energy production. Continuous operation often impacts their functionality, necessitating effective maintenance methods. This study employs vibration analysis to monitor the health and detect potential faults in the motor and coal crusher. Vibration values, measured under load and no-load conditions, were analyzed in vertical, horizontal, and axial directions using ISO 10816 standards. Results show that while vibration levels remained within safe limits, increased readings in specific areas, particularly at the Crusher NDE in axial direction under load conditions, suggest potential issues like misalignment or bearing wear. Prominent vibration peaks at lower frequencies in the spectrum further support these findings. This research underscores the importance of routine vibration monitoring to preemptively address machine faults, ensuring optimized operation and minimized downtime. Vibration analysis proves effective for predictive maintenance and operational reliability in coal processing systems.