Maintenance Challenges Research Articles

Challenges during installation and maintenance of water delivery infrastructure: a citizen perspective

The installation and maintenance of water infrastructure in South African cities poses a challenge to communities, municipalities, and installation contractors, who face problems such as inadequate access to homes, traffic jams, inadequately barricaded trenches, leaking water pipes, water supply cut-offs, vandalism, and theft of water. The research problem, therefore, involves an inadequate understanding of what drives these problems and how to mitigate them through better management. The aim of this study was to investigate the challenges of managing water infrastructure as perceived by the citizens of a South African municipality. The study was a descriptive and cross-sectional survey, with data collected, via a literature derived, emailed questionnaire, from a self-selected non-probability sample of 402 City of Tshwane residents. Analysis, via SPSS Version 27, used descriptive and inferential statistics. The findings revealed that both the municipality and the contractors contributed to the challenges experienced by citizens, with maintenance challenges being more serious than installation challenges. Municipal and political challenges were the main drivers, with political ownership of construction companies and political interference being the biggest problems. The findings contributed to a better understanding of the challenges inhibiting effective installation and maintenance of water infrastructure, and are important to the municipality, and the community at large, because they contribute to a better understanding of service delivery from the citizens’ perspectives.

Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm

Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes.

Road Maintenance Challenges: The Greatest Obstacle to Sustainable Development in South Sudan

Road infrastructure plays a crucial role in the sustainable development of any nation. It connects communities, facilitates trade, and enables essential service delivery. However, road maintenance is one of the most significant challenges hindering sustainable development in South Sudan. The poor roads hinder economic growth, access to essential services, and social cohesion and are in dire need of repair and upkeep. Thus, I developed this qualitative multiple-case study to explore challenges that hinder road maintenance in South Sudan. I collected data through document review and semi-structured interviews with four road engineers, and then thematically analyzed the gathered data. Three themes emerged as the most significant challenges facing road maintenance in South Sudan: a lack of functional road maintenance institutions, a lack of legal framework supporting road maintenance, and a lack of dedicated funds for the maintenance of roads. Key recommendations include making road construction and maintenance institutions function, establishing legal frameworks to support road construction and maintenance in the country, and establishing a Road Fund agency to provide the necessary resources to support road construction and maintenance in the country. The implications for positive social change include facilitating access to essential services such as education, healthcare, employment, and economic prosperity.

Experimental based time-dependent reliability assessment of corrosion initiation for PC girders in frozen marine environment

Chloride induced corrosion is the main reason for the deterioration of concrete structures in marine environments, and the prediction of corrosion initiation life is one of the greatest challenges for preventative maintenance. To conduct a corrosion initiation assessment of PC girders in frozen marine environment and support preventative maintenance, a time-dependent reliability assessment method is developed. Long-term exposure experiments and in-house tests are combined to calibrate the chloride diffusion model, and then a probabilistic diffusion model is proposed based on the randomness of the key model parameters. Through the analysis of the target reliability index of corrosion initiation, the risk-function based time-dependent reliability assessment method is developed. Finally, a case study is introduced to illustrate the application of the proposed method. It is found that the surface chloride concentration on concrete structures in frozen marine environments increases at the beginning of service life and it tends to be stabilize when the service time is around 9–10 years. The age reduction factor of chloride diffusion can be described as the linear function of cementitious composition. The target reliability indices of corrosion initiation are suggested as 2.46, 1.50 and 1.13 for the referenced periods of 1, 10 and 20 years. The corrosion initiation life is very sensitive to the tensile stress level of concrete, and it decreases linearly with the increasement in tensile stress when the stress is not higher than 2 MPa.

Study of Technological Advancement and Challenges of Façade System for Sustainable Building: Current Design Practice

Currently, several façade systems exist to enable sustainable building design. The biggest challenges for façade designers are to identify new technology and effective, sustainable systems that enable high structural and sustainable performance while producing a good aesthetic. Therefore, this paper aims to review the performance of existing façade systems for sustainable building designs and their limitations. Among modern façade systems, Double Skin Façades (DSF) show promise for energy efficiency, indoor air quality, and aesthetics. However, they face challenges like sound transmission between floors, higher initial costs, and outer skin vibrations. Furthermore, adaptive façades gained popularity for their active methods of achieving energy performance and comfort benefits but encountered complexities in design and construction, demanding codes and standards. Green wall systems enhance air quality and aesthetics, while photovoltaic façade systems reduce electricity costs, but both systems face higher initial costs and maintenance challenges. The review indicates that to produce a sustainable building design, architects, engineers, and builders must consider a sustainable façade system that enables high energy efficiency, less cost, better occupant comfort, and fewer environmental impacts.

Fascial plane mapping for superficial tumor resection in dogs. Part III: Hindlimb and pelvis.

To categorize the fascial planes and the intersections of these fascial planes in the hindlimb of the dog to facilitate preoperative planning for superficial cancers. Qualitative anatomical study. Four male and five female mixed breed dogs, weighing ~15-35 kg. Skin and subcutaneous fat were removed, and fascial planes were explored to determine borders and quality. Fascia was categorized as type I (discrete sheets), type II (adhered to thin muscles), type III (adhered to thick muscles), or type IV (associated with periosteum). Digital modification of specimen photographs was performed to map tissues. Differences in dogs were noted due to either size or sex but were sufficiently minor to allow mapping. Fasciae of the hindlimb were predominantly type II or III, with type I fascia primarily at the lateral gluteal region, fascia lata, and lateral crus. Type IV fascia was seen at the iliac wing, ischium, patella, tibial tuberosity, medial tibia, distal crus, and pes. Fascia for surgical use was thin or absent at the ischiorectal fossa, femoral triangle, extensor mechanism, medial and distal crus, and pes. Intersections and tissues at the ventral perineum may also pose challenges for maintenance of a deep margin. Fascial types and integrity of the hindlimb varied with location, with the perineum, cranial stifle, and distal limb presenting the greatest challenges. These images may be used to guide both therapeutic decision-making and intraoperative excision of superficial tumors of the hindlimb and pelvis.

Machine-Learning Approach for Prognosis of Oscillating Water Column Wave Generators

Wave excitations cause structural vibrations on the Oscillating Water Columns (OWC) lowering the power generated and reducing the life expectancy. The problem of generator deterioration has been considered for the Mutriku MOWC plant and a machine learning-based approach for prognosis and fault characterization has been proposed. In particular, the use of k-Nearest Neighbor (kNN) models for predicting the time to failure of OWC generators has been proposed. The analysis is based on data collected from sensors that measure various operational parameters of the turbines. The results demonstrate that the proposed kNN model is an excellent choice for reducing maintenance costs by enabling maintenance scheduling months in advance. The model's high accuracy in predicting generator failures allows for timely and cost-effective maintenance, preventing costly breakdowns and improving turbine efficiency. The results highlight the potential of machine learning-based approaches for addressing maintenance challenges in the energy sector and underscore the importance of proactive maintenance strategies in reducing operational costs and maximizing energy production.

Exploring Optimal Charging Strategies for Off-Grid Solar Photovoltaic Systems: A Comparative Study on Battery Storage Techniques

This paper presents a comparative analysis of different battery charging strategies for off-grid solar PV systems. The strategies evaluated include constant voltage charging, constant current charging, PWM charging, and hybrid charging. The performance of each strategy is evaluated based on factors such as battery capacity, cycle life, DOD, and charging efficiency, as well as the impact of environmental conditions such as temperature and sunlight. The results show that each charging strategy has its advantages and limitations, and the optimal approach will depend on the specific requirements and limitations of the off-grid solar PV system. This study provides valuable insights into the performance and effectiveness of different battery charging strategies, which can be used to inform the design and operation of off-grid solar PV systems. This paper concludes that the choice of charging strategy depends on the specific requirements and limitations of the off-grid solar PV system and that a careful analysis of the factors that affect performance is necessary to identify the most appropriate approach. The main needs for off-grid solar photovoltaic systems include efficient energy storage, reliable battery charging strategies, environmental adaptability, cost-effectiveness, and user-friendly operation, while the primary limitations affecting these systems encompass intermittent energy supply, battery degradation, environmental variability, initial investment costs, fluctuations in energy demand, and maintenance challenges, emphasizing the importance of careful strategy selection and system design to address these factors. It also provides valuable insights for designing and optimizing off-grid solar PV systems, which can help to improve the efficiency, reliability, and cost-effectiveness of these systems.

NAD+, Axonal Maintenance, and Neurological Disease.

Significance: The remarkable geometry of the axon exposes it to unique challenges for survival and maintenance. Axonal degeneration is a feature of peripheral neuropathies, glaucoma, and traumatic brain injury, and an early event in neurodegenerative diseases. Since the discovery of Wallerian degeneration (WD), a molecular program that hijacks nicotinamide adenine dinucleotide (NAD+) metabolism for axonal self-destruction, the complex roles of NAD+ in axonal viability and disease have become research priority. Recent Advances: The discoveries of the protective Wallerian degeneration slow (WldS) and of sterile alpha and TIR motif containing 1 (SARM1) activation as the main instructive signal for WD have shed new light on the regulatory role of NAD+ in axonal degeneration in a growing number of neurological diseases. SARM1 has been characterized as a NAD+ hydrolase and sensor of NAD+ metabolism. The discovery of regulators of nicotinamide mononucleotide adenylyltransferase 2 (NMNAT2) proteostasis in axons, the allosteric regulation of SARM1 by NAD+ and NMN, and the existence of clinically relevant windows of action of these signals has opened new opportunities for therapeutic interventions, including SARM1 inhibitors and modulators of NAD+ metabolism. Critical Issues: Events upstream and downstream of SARM1 remain unclear. Furthermore, manipulating NAD+ metabolism, an overdetermined process crucial in cell survival, for preventing the degeneration of the injured axon may be difficult and potentially toxic. Future Directions: There is a need for clarification of the distinct roles of NAD+ metabolism in axonal maintenance as contrasted to WD. There is also a need to better understand the role of NAD+ metabolism in axonal endangerment in neuropathies, diseases of the white matter, and the early stages of neurodegenerative diseases of the central nervous system. Antioxid. Redox Signal. 39, 1167-1184.

Validating a Safety Culture Toolkit for Aviation

Safety culture has ranked as a top human factors challenge for aviation maintenance, but there is a lack of actionable guidance for properly assessing and improving safety culture. As part of its efforts to promote a positive safety culture in aviation maintenance, the researchers developed and validated a new safety culture toolkit. The toolkit consists of a customizable survey, scoring guidance, and a roadmap for safety culture improvement. The data from n = 987 participants across five participating organizations supported the Job Demands-Resources (JD-R) model. Safety culture was a significant predictor of both Individual and Organizational Outcomes, with Individual Outcomes partially mediating the impact of safety culture on Organizational Outcomes. These results provided initial evidence of the content and predictive validity.

Blockchain‐based data deduplication using novel content‐defined chunking algorithm in cloud environment

AbstractThe cloud environment is inherently dynamic as users are added immensely in a short duration. It is indeed difficult to manage such user profiles and associated data. Meanwhile, the cloud data expand at a twofold‐to‐threefold rate on average, making storage space management and data integrity maintenance a mandatory task but also risky. The main approaches for addressing these data maintenance challenges in a cloud context are deduplication and data protection. In order to manage storage space, finding and removing identical copies of the same data from the cloud are possible, resulting in a reduction in the amount of storage space needed. Furthermore, duplicate copies are considerably reduced in cloud storage owing to data deduplication. Here, a decentralized ledger public blockchain network is introduced to protect the Integrity of data stored in cloud storage. This research proposes data deduplication using speedy content‐defined Chunking (SpeedyCDC) algorithm in the public blockchain. Many people and businesses outsource sensitive data to remote cloud servers because it considerably eliminates the hassle of managing software and infrastructure. However, the ownership and control rights of users data are nonetheless divided because it is outsourced to cloud storage providers (CSPs) and kept on a distant cloud. As a result, users have a great deal of difficulty in verifying the Integrity of sensitive data. Analysis using datasets from Geospatial Information Systems (GIS) revealed that the throughput increased by 5%–6% over that of the fastCDC technique, which offered Integrity since a blockchain network secured it.

Model predictive control strategy in waked wind farms for optimal fatigue loads

With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and the load equalization. The controller is evaluated in a medium-fidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor.

Application of hydrogeophysics and diagnostic plots in setting up a sustainable managed aquifer recharge scheme, West Coast, South Africa

The lack of using hydrogeophysics and hydraulic data in setting up a sustainable managed aquifer recharge scheme leads to challenges in operations and maintenance of such infrastructures. Managed aquifer recharge is an engineering and technological based water management approach that is used to maximize storage and improve groundwater security. However, this approach is under-utilized in many countries and South Africa is no exception. This study aimed at improving knowledge on aquifer dynamics for managed aquifer recharge scheme using hydrogeophysics and diagnostic plots. This study argued that integrating hydrogeophysics data and diagnostic plots for setting up managed aquifer recharge schemes lead to improved operations and maintenance through improved scientific knowledge on the dynamics of the aquifer. SkyTEM and electrical resistivity tomography data were used to delineate subsurface structures. Pumping test data were used to develop diagnostic plots, for identifying flow regimes and boundary conditions in the proposed managed aquifer recharge area. Results from geophysics investigation inferred pathways for potential managed aquifer recharge and areas to avoid. The integration of hydrogeophysics and diagnostic plots provided a more practical interpretation for the study area. It is recommended that hydrogeophysics investigation should be carried out before and after setting managed aquifer recharge scheme as it leads to enhanced decisions on groundwater usage, monitoring and management.

Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph

In the manufacturing industry, a hydraulic system harnesses liquid fluid power to create powerful machines. Under the trend of Industry 4.0, the predictive maintenance of hydraulic systems is transforming to more intelligent and automated approaches that leverage the strong power of artificial intelligence and data science technologies. However, due to the knowledge-intensive and heterogeneous nature of the manufacturing domain, the data and information required for predictive maintenance are normally collected from ubiquitous sensing networks. This leads to the gap between massive heterogeneous data/information resources in hydraulic system components and the limited cognitive ability of system users. Moreover, how to capture and structure useful domain knowledge (in a machine-readable way) for solving domain-specific tasks remains an open challenge for the predictive maintenance of hydraulic systems. To address these challenges, in this paper we propose a virtual knowledge graph-based approach for the digital modeling and intelligent predictive analytics of hydraulic systems. We evaluate the functionalities and effectiveness of the proposed approach on a predictive maintenance task under real-world industrial contexts. Results show that our proposed approach is capable and feasible to be implemented for digital modeling, data access, data integration, and predictive analytics.

Enhancing Barn Hygiene through Smart Farming: A Goat Farm Case Study in Besijangkang Using IoT

Besijangkang farms currently rely on conventional management methods, including manual cleaning with broomsticks and inadequate lighting. The scattered droppings and urine on the floor pose challenge for efficient farm maintenance. To address these issues, a solution combining technology and conventional cage management has been proposed to allow remote access for breeders. Researchers have implemented Internet of Things (IoT) utilizing the ESP8266 NodeMCU microcontroller connected to Firebase Web. The integration of IoT in smart farming tools has proven successful and functional. The app enables remote control of the lights through designated buttons. A critical indicator for the goat farm is the urine volume, reaching full capacity at 778 liters, which triggers the illumination of the light near the component box. Furthermore, the DC motor and conveyor effectively manage the dirt load at 09.00, fulfilling their designated functions. By adopting IoT-based smart farming tools, Besijangkang farms can improve their hygiene system, enhance management efficiency, and empower breeders with remote accessibility, revolutionizing traditional farming practices for better productivity and sustainability.

Implementasi Sistem Monitoring Tanaman Hidroponik Menggunakan Metode Fuzzy Sugeno

Farming using hydroponic systems has emerged as an effective alternative to address the problem of limited agricultural land. However, hydroponic farming requires strict maintenance and control. In light of these challenges, the development of monitoring technology is necessary to assist in monitoring and controlling hydroponic systems. The development of a monitoring system is carried out to address the challenges of maintenance and control in hydroponic farming through the development of effective and accurate monitoring technology, thus enhancing the overall productivity and success of hydroponic plant cultivation. This study aims to develop a monitoring system using Arduino Mega 2560, Wemos D1 R2, E201 sensor, DHT22 sensor, HC-SR04 sensor, and BH1750 sensor. In this research, the fuzzy logic method was applied to generate outputs for the water pump actuator, pH up pump, and pH down pump. The HC-SR04 and E201 sensor data served as fuzzy inputs, while the Arduino Mega 2560 functioned as the fuzzy data processor, and the Wemos D1 R2 was responsible for sending data to the server via the internet. The data successfully stored in the server's database would be displayed on the monitoring dashboard. The HC-SR04 sensor testing results showed an average %error of 0% and an accuracy of 100%. The E201 sensor exhibited an average %error of 0.07% and an accuracy of 99.33%. The DHT22 sensor had an average %error of 0.05% and an accuracy of 99.49% for temperature data, while for humidity data, it had an average %error of 2.58% and an accuracy of 75.23%. The BH1750 sensor had an average %error of 0.13% and an accuracy of 99.66%. The results of this study demonstrate that the hydroponic plant monitoring system using the described devices and methods can be implemented successfully.

An Optimal Opportunistic Maintenance Planning Integrating Discrete- and Continuous-State Information

Information-driven group maintenance is crucial to enhance the operational availability and profitability of diverse industrial systems. Existing group maintenance models have primarily concentrated on a single health criterion upon maintenance implementation, where the fusion of multiple health criteria is rarely reported. However, this is not aligned with actual maintenance planning of multi-component systems on many occasions, where multi-source health information can be integrated to support robust decision making. Additionally, how to improve maintenance effectiveness through a scientific union of both scheduled and unscheduled maintenance remains a challenge in group maintenance. This study addresses these research gaps by devising an innovative multiple-information-driven group replacement policy for serial systems. In contrast to existing studies, both discrete-state information (hidden defect) and continuous degradation information are employed for group maintenance planning, and scheduled postponed maintenance and unscheduled opportunistic maintenance are dynamically integrated for the first time to mitigate downtime loss. To be specific, inspections are equally spaced to reveal system health states, followed by the multi-level replacement implemented when either (a) the degradation of the continuously degrading unit reaches a specified threshold, or (b) the age of the multi-state unit since the defect’s identification reaches a pre-set age (delayed replacement). Such scheduling further enables the implementation of multi-source opportunistic replacement to alleviate downtime. The Semi-Markov Decision Process (SMDP) is utilized for the collaborative optimization of continuous- and discrete-state thresholds, so as to minimize the operational costs. Numerical experiments conducted on the critical structure of circulating pumps verify the model’s applicability.

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Offshore Wind Power Systems (OWPS) offer great energy and environmental advantages, but also pose significant Operation and Maintenance (O&M) challenges. In this survey, we analyze these challenges and propose some optimization strategies and technologies for OWPS comprehensively. The existing literature review mainly focuses on a certain field of offshore wind power O&M, but lacks a comprehensive introduction to offshore wind power. We consider the energy efficiency, reliability, safety, and economy of OWPS from various aspects, such as offshore wind and wave energy utilization, offshore wind turbine components, and wind power operation parameters, and compare them with onshore wind power systems. We suggest that OWPS can benefit from advanced design optimization, digital twin, monitoring and forecasting, fault diagnosis, and other technologies to enhance their O&M performance. This paper aims to provide theoretical guidance and practical reference for the technological innovation and sustainable development of OWPS.

A comprehensive review on deploying robotics application in telecom network tower's field maintenance: Challenges with current practices and feasibility analysis for robotics implementation

AbstractThis survey article highlights the difficulties in the field maintenance of telecommunication towers. It critically analyses the main features of the deployment of robots to maintain telecommunication towers. The growing demand for mobile connectivity poses the need for more towers, and the subsequent problem of network maintenance becomes more critical. Most tower maintenance is required work at height; therefore, height‐related risks are more frequent. A rigorous review is conducted, and the growth of the telecommunications network and key on‐site maintenance challenges are analyzed. Despite numerous challenges, these towers are maintained manually by riggers (certified climbers) worldwide. It raises the question, Is it possible to implement automation by robots for the maintenance of telecommunications towers? The feasibility analysis to deploy the robots is conducted systematically. To access the tower through a robot, detailed information on the type of towers, the climbing arrangements available on the existing towers, and the necessary operations to be carried out at the height is collected. A critical analysis of the climbing robots currently available in the literature, their grasping technology, and control algorithms is performed. The opinion of experts in the telecommunication industry is very helpful in identifying the requirements of robotic systems. The design attributes especially needed for the climbing robot, and the execution of the maintenance in height are highlighted. Due justification is given for deploying robots for field maintenance of telecom towers. The recommended methodology for designing an automation system helps research in the field of maintenance of telecom towers through robots, which could bring a remarkable solution to the telecom sector.

Implementing green facades: A step towards sustainable smart buildings

As cities around the globe strive towards becoming “smart” and sustainable, the integration of green facades into smart building design has emerged as a viable solution to address several environmental challenges. This paper conducts a comprehensive review of current research and applications in green facades, analyzing their role in enhancing energy efficiency, improving indoor air quality, and contributing to overall building sustainability. The impact of green facades on reducing energy consumption, through natural insulation and shading, is explored in-depth, alongside their role in air purification and enhancing the aesthetic appeal of urban environments. Despite the potential of green facades, several gaps between theory and practice exist, including challenges in implementation, maintenance, and cost-effectiveness. This paper identifies these barriers and provides a robust discussion of potential solutions, which include technological innovations, policy support, and public awareness initiatives. Ultimately, the integration of green facades in smart buildings presents a significant step forward in creating sustainable urban environments, necessitating further research and action in this realm.