Allocation Of Maintenance Resources Research Articles

Investigating the Effects of Ageing on Transmission System Dependability Through the Use of an Artificial Neural Network

As power transmission systems age, their dependability becomes increasingly critical for maintaining reliable electricity supply. However, accurately predicting the impact of ageing on system dependability remains a challenging task due to the complex interactions among various components and environmental factors. In this study, we propose the utilization of an artificial neural network (ANN) approach to investigate the effects of ageing on transmission system dependability. The ANN model is trained on historical data encompassing a wide range of parameters including equipment age, maintenance records, environmental conditions, and system performance metrics. By capturing the nonlinear relationships within the data, the ANN can effectively model the intricate dependencies between ageing and system dependability. Through comprehensive simulations and sensitivity analyses, we demonstrate the capability of the ANN to forecast the degradation of transmission system dependability over time. Moreover, the model enables the identification of key factors driving system reliability decline, thereby facilitating proactive maintenance strategies and resource allocation.

Developing a bi-objective maintenance optimization model for process industries by prioritizing resilience and robustness using dynamic Bayesian networks

In this paper, we developed a new model that combines resilience concept with robust maintenance resource allocation, and team assignment strategies to evaluate system performance after disruption. This model offers insights for proactive preparation before disruptive events and facilitates optimal decision-making in the aftermath of such disruptions. Our approach involves a non-linear programming model that operates at both strategic and operational decision levels. It aims to simultaneously minimize total maintenance costs while maximizing resilience by determining the allocation of backup resources to system components before a disruption occurs and suggesting emergency maintenance team assignments post-disruption, all while adhering to resilience constraints.We quantify resilience using a dynamic Bayesian Network method and Markov process, utilizing metrics based on absorption, adaptation, and restoration performance (reliability) curves. We propose a bi-objective optimization model and leverage the genetic algorithm NSGA-III to obtain Pareto-optimal solutions. To address the inherent uncertainty of various disruption scenarios, we generate a range of possible scenarios and employ robust optimization techniques to derive solutions that can withstand these uncertainties. To demonstrate the effectiveness of our proposed model, we conduct a case study involving a City Gate Station in agas industry.

Alanine metabolism mediates energy allocation of the brown planthopper to adapt to resistant rice

IntroductionDuring the adaptation to host plant resistance, herbivorous insects faced the challenge of overcoming plant defenses while ensuring their own development and reproductive success. To achieve this, a strategic allocation of energy resources for detoxification and ecological fitness maintenance became essential. ObjectiveThis study aimed to elucidate the intricate energy allocation mechanisms involved in herbivore adaptation that are currently poorly understood. MethodsThe rice Oryza sativa and its monophagous pest, the brown planthopper (BPH), Nilaparvata lugens were used as a model system. An integrated analysis of metabolomes and transcriptomes from different BPH populations were conducted to identify the biomarkers. RNA interference of key genes and exogenous injection of key metabolites were performed to validate the function of biomarkers. ResultsWe found that alanine was one of the key biomarkers of BPH adaptation to resistant rice variety IR36. We also found that alanine flow determined the adaptation of BPH to IR36 rice. The alanine aminotransferase (ALT)-mediated alanine transfer to pyruvate was necessary and sufficient for the adaptation. This pathway may be conserved, at least to some extent, in BPH adaptation to multiple rice cultivars with different resistance genes. More importantly, ALT-mediated alanine metabolism is the foundation of downstream energy resource allocation for the adaptation. The adapted BPH population exhibited a significantly higher level of energy reserves in the fat body and ovary when fed with IR36 rice, compared to the unadapted population. This rendered the elevated detoxification in the adapted BPH and their ecological fitness recovery. ConclusionOverall, our findings demonstrated the crucial role of ALT-mediated alanine metabolism in energy allocation during the adaptation to resistant rice in BPH. This will provide novel knowledge regarding the co-evolutionary mechanisms between herbivores and their host plants.

Wastewater Management in Tomato Factories

Tomato (Lycopersicon esculentum) is a globally consumed vegetable with a significant role in the food industry. In 2021, global tomato production exceeded 189.1 million metric tons, emphasizing its economic importance. This research delves into the challenges posed by wastewater generated during tomato processing and explores sustainable strategies for managing it. Case studies were conducted in tomato processing plants in California, Kadawa (Kura LGA, Kano State), and Kumo (Gombe State) to assess their wastewater management practices. The findings revealed a lack of uniform wastewater collection systems in the studied factories, highlighting the need for improved environmental sustainability. As Bauchi State's population and industrialization grow, addressing the wastewater issue becomes increasingly urgent. Inadequate wastewater management can have detrimental effects on human health, water supplies, and the environment. To mitigate these challenges, this study proposes the integration of sustainable techniques in tomato processing factories. The objectives include quantifying wastewater generation, designing a functional factory layout applicable to other food processing industries, and creating a factory design that promotes sustainable water use. Site selection criteria were established, focusing on accessibility, labor availability, utilities, expansion potential, topography, and environmental impact. After a thorough evaluation, a site in Hardawa, Misau Local Government Area of Bauchi State, was identified as the most suitable location for the proposed tomato processing factory. The factory's design brief includes provisions for production facilities, administrative blocks, and worker facilities. Space requirements were determined based on the anticipated activities within each section. The proposed design emphasizes water harvesting and recycling to reduce wastewater generation and promote sustainability. In conclusion, this research underscores the importance of addressing wastewater management in tomato processing factories. By implementing sustainable design principles, factories can minimize environmental impact, enhance operational efficiency, and demonstrate commitment to ethical business practices. The proposed design offers a comprehensive solution to the wastewater challenge, ensuring compliance with environmental standards and safeguarding natural resources. Recommendations include collaboration between factory management and government authorities, the adoption of sustainable design approaches in future tomato processing facilities, and the allocation of resources for ongoing maintenance. Embracing these recommendations will contribute to more responsible and environmentally conscious tomato processing practices.

A New Methodology for the Prioritization of Visual Inspections of Bridges and Viaducts

AbstractThe assessment of risk conditions of bridges and viaducts allows management authorities to ensure the safety of the users and the proper allocation of economic resources for maintenance and retrofitting interventions. A common circumstance in practice regards the lack of information as well as the need to update it, causing delays in the risk evaluation and, consequently, in the entire decision‐making process. Especially in the managing of large bridge assets, the retrieval/updating of information can be costly, both in terms of time and economic resources. In this regard, the execution of visual inspections to determine the defect level of bridges and viaducts is the most resource‐intensive operation on the part of the management authorities. In this paper, a new methodology to prioritize visual inspections of bridges according to their risk condition and operating costs is proposed and illustrated with a real case study. A novel aspect of the proposed methodology regards the use of information gain criteria to refine the outcomes from the risk assessment and prioritization process. Finally, the outcomes of the methodology are implemented in a GIS framework to ensure high interoperability and facilitate the construction of intuitive risk maps to assist inspection plans.

Single-Machine Maintenance Activity Scheduling with Convex Resource Constraints and Learning Effects

In this paper, the single-machine scheduling problems under the common and slack due date assignments are studied, where the actual processing time of the job needs to consider some factors, such as convex resource allocation, maintenance activity, and learning effects. The goal of this study is to find the optimal sequence, maintenance activity location, resource allocation and common due date (flow allowance). The objective function is (1) to minimize the sum of scheduling cost (including the weighted sum of earliness, tardiness and common due date (flow allowance), where the weights are position-dependent weights) and resource consumption cost, and (2) to minimize the scheduling cost under the resource consumption cost which is bounded. We prove that these problems can be solved in polynomial time.

A state-of-the-art digital factory integrating digital twin for laser additive and subtractive manufacturing processes

PurposeThis study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.Design/methodology/approachThis study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.FindingsA comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.Originality/valueBased on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Energy allocation trade-offs as a function of age in fungiid corals

To compete effectively, living organisms must adjust the allocation of available energy resources for growth, survival, maintenance, and reproduction throughout their life histories. Energy demands and allocations change throughout the life history of an organism, and understanding their energy allocation strategies requires determination of the relative age of individuals. As most scleractinian corals are colonial, the relationship between age and mass/size is complicated by colony fragmentation, partial mortality, and asexual reproduction. To overcome these limitations, solitary mushroom corals, Herpolitha limax from Okinawa, Japan and Fungia fungites from Okinawa and the Great Barrier Reef (GBR), Australia, were used to investigate how energy allocation between these fundamental processes varies as a function of age. Measurements of the relative growth, biochemical profiles, fecundity of individuals of different sizes, and the settlement success of their progeny have revealed physiological trade-offs between growth and reproduction, with increasing body mass ultimately leading to senescence. The importance of energy allocation for reproduction led us to examine the reproductive strategies and sex allocation in the two studied species. In the present study, the smallest individuals of both species studied were found to invest most of their energy in relative growth, showing higher lipid and carbohydrate content than the later stages. In medium-sized corals, this pattern was overturned in favour of reproduction, manifesting in terms of both the highest fecundity and settlement success of the resulting brooded larvae. Finally, a phase of apparent senescence was observed in the largest individuals, characterized by a decrease in most of the parameters measured. In addition, complex reproductive plasticity has been revealed in F. fungites in the GBR, with individual females releasing eggs, embryos, planulae, or a combination of these. These data provide the most direct estimates currently available for physiological, age-related trade-offs during the life history of a coral. The unusual reproductive characteristics of the GBR F. fungites indicate previously unknown layers of complexity in the reproductive biology of corals and have implications for their adaptive potential across a wide geographical scale.

Maintenance resource allocation – the business analytics usage in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the applications of usage of business analytics in maintenance resources allocation. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics with condition monitoring technologies heralds a transformative shift in maintenance resource allocation. Through the utilization of real-time data from sensors and monitoring devices, organizations can craft dynamic and adaptive maintenance strategies. These strategies, grounded in the current equipment condition, strike a nuanced balance between preventive and corrective actions. Business analytics enriches decision-making by factoring in critical elements such as equipment importance, operational impact, resource availability, and budget constraints. This integration instigates a paradigm shift, fostering proactive, adaptive, and efficient resource allocation, resulting in heightened asset reliability, diminished downtime, and amplified operational performance. The evolving synergy between business analytics and maintenance practices is becoming integral to the future of asset management and industrial operations. The diverse applications of business analytics in maintenance, as outlined in Table 1, underscore its versatility, encompassing predictive maintenance, condition monitoring, asset performance management, work order prioritization, resource optimization, cost-benefit analysis, inventory management, and performance metrics monitoring. Concurrently, the adoption of advanced software solutions in Industry 4.0 conditions, exemplified by IBM Maximo Asset Management, SAP Intelligent Asset Management, and Fiix CMMS, reflects a commitment to efficiency and innovation in maintenance resource allocation. Despite the substantial advantages, addressing challenges outlined in Table 4, including data quality, integration complexities, implementation costs, and skill development, is crucial. These challenges underscore the necessity for a strategic and holistic implementation approach that considers technology, personnel training, and organizational readiness. In essence, the evolution of maintenance resource allocation through business analytics signifies a data-driven revolution poised to optimize current operations and position organizations for sustained success amid rapid technological advancements and the transformations of Industry 4.0. Originality/value: Detailed analysis of business analytics in the case of maintenance resource allocation. Keywords: business analytics, Industry 4.0, digitalization, artificial intelligence, real-time monitoring; maintenance resource allocation. Category of the paper: literature review.

An Optimization Method for Maintenance Resource Allocation in Electrified Railway Catenary Systems

The traditional preventive maintenance plans of electrified railways are mainly formulated according to historical maintenance records and the experience of maintenance technicians. With the increase of the maintenance workload of catenary systems, traditional maintenance decision-making modes are hard to accommodate the demand for the high-efficiency and economical operation of catenary systems. To cater for the on-site maintenance demands, a conceptual framework towards the cost-effective optimization of catenary maintenance is proposed in this paper. The framework includes investigating and sorting the physical quantities in practical maintenance operations, modeling the maintenance operation process, establishing and solving the optimization model of the maintenance operator allocation with multiple optional objectives, and developing the human-machine application system, namely, the resource allocation system (RAS) with both hardware and software for electrified railway catenary maintenance. Field application shows that the implementation of RAS is superior to the current maintenance scheme in maintenance efficiency and cost, which can provide decision support for the scientific formulation of catenary system maintenance scheme, and promote the intelligent development of current maintenance modes.

Maintenance prioritisation framework for Taiwan’s pedestrian bridges

This paper presents a maintenance prioritisation framework for Taiwan’s 1268 pedestrian bridges. It aims to standardise their inspection and maintenance, as Taiwan’s current bridge management specifications and manuals only apply to its highway and railway bridge infrastructure. It is designed to assess the structural safety and serviceability of six pedestrian bridge types by visual inspection. To assign weight factors to the structural safety and serviceability components of the identified bridge types, two rounds of Delphi method interviews with bridge experts were conducted. The framework is centred on a weighted average condition assessment method that integrates expert-determined weight factors with visual inspection results to calculate a condition index (PBCI). Pedestrian bridges are ranked and classified based on their PBCIs to set appropriate inspection frequency and scope, and to inform maintenance resource allocation. The application of the framework to five pedestrian bridges in Taoyuan City demonstrated its practicality and value for bridge authorities tasked with managing large inventories of pedestrian bridges.

Improving reliability with optimal allocation of maintenance resources: an application to power distribution networks

Improving reliability with optimal allocation of maintenance resources: an application to power distribution networks

Performance-based maintenance analysis and resource allocation in irrigation networks

The irrigation network is a critical infrastructure network consisting of reservoirs, canals, and croplands. However, the operation of irrigation networks is often disrupted by natural disasters like droughts, which can lead to serious losses. Timely and effective maintenance can minimize the yield losses. Many maintenance strategies have been proposed to improve the efficiency of maintenance resource utilization and reduce costs. But in the existing literature, the maintenance of irrigation networks under the influence of droughts is still under-explored. To bridge this gap, this paper proposes a maintenance metric for improving the performance of an irrigation network. A novel optimum maintenance efficiency model is then developed to get the optimal resource allocation. A case study, which investigates an irrigation network consisting of 27 nodes, is used to verify the practicality and effectiveness of the proposed method.

A Text Mining Framework for Analyzing Change Impact and Maintenance Effort of Software Bug Reports

Software practitioners often strive to achieve a “bug-free” software, though, it is a myth. Software Bug Categorization (SBC) models, which assigns levels (viz. “low”, “moderate” or “high”) to a software bug aid effective bug management. They assist in allocation of proper maintenance resources for bug elimination to improve software quality. This study proposes the development of SBC models that allocate levels on the basis of three software bug aspects i.e., maintenance effort required to correct a bug, its change impact and the combined effect of both of these. In order to develop SBC models, we use text mining approach, which extracts relevant features from bug descriptions and relates these features with different software bug levels. The results of the study indicate that the categorization of software bugs in accordance with maintenance effort and change impact is possible. Furthermore, the combined approach SBC models were also found to be effective.

An IoT-based maintenance framework for irrigation and drainage water management system at regional scale

The management of the geographically distributed assets of irrigation and drainage water grids compels a challenging alignment of operations like maintenance plans, resource allocation, and spare parts inventory management systems. We propose an IoT-based maintenance framework to optimize reliability, management costs, and logistics and increase the resilience and sustainability of the irrigation and water management network. The proposed framework provides an interface between the centralized inventory system and the maintenance and logistics operations carried out throughout the network. Information exchange is supported by bar code and RFID technologies integrated within the IT system of the company. A Maintenance Management Software (MMS) able to store assets' physical hierarchy data and inventory data is the supporting tool that fuels the framework. The framework is applied to a regional drainage water system in the Emilia-Romagna region (Italy), improving the visibility of the infrastructural network and enabling tailored and data-driven preventive maintenance plans. The combination of the components' availability, logistics and maintenance costs, failure trend, and impact on the overall network contribute to establishing new methods for assessing the threshold of service level (i.e., reliability) to guarantee for each material.

Results of Parallel-Machine Scheduling Model with Maintenance Activity considering Time-Dependent Deterioration, Delivery Times, and Resource Allocation

This paper investigates parallel-machine scheduling models with maintenance activity, delivery times, time-dependent deterioration, and resource allocation. We consider two forms of the problem: the first is to minimize the sum of total completion times, total machine loads, the total absolute deviation of job completion times, and the total resource allocation; the second is to minimize the sum of total waiting times, total machine loads, the total absolute deviation of job waiting times, and the total resource allocation. The problems are proved to be solvable in polynomial time.

Study on optimization of inspection mechanism of concrete beam bridge.

China is shifting from the stage of large-scale bridge construction to the stage of equal emphasis on the construction and maintenance of bridges. The problems of low efficiency and high cost in bridge inspection are becoming more and more prominent, which threaten people’s life safety. In this paper, the deterioration state prediction model of concrete beam bridge under Boosting DT C5.0 was established as the basis, and optimization suggestions were put forward in terms of bridge inspection standards and processes, which aims to perfect the bridge inspection mechanism, realize the fine management of the bridge and prolong the service life of the bridge. Research shows that: first, the bridge inspection standard with a single index should be improved into the bridge inspection standard with multiple indexes, so as to scientifically determine the bridges that need to be inspected and optimize the allocation of maintenance resources. Second, the bridge deterioration state prediction model is used to add a screening mechanism for the bridge in the inspection plan, which can effectively reduce the workload of bridge inspection and enhance the inspection efficiency. Third, the deterioration phenomenon of coexistence between adjacent traffic assets should be fully considered and the linkage inspection mechanism of adjacent traffic assets should be established to improve the effect of bridge inspection.

Impacts of service feature on vulnerability analysis of high-speed rail network

While previous literatures have analyzed the vulnerability of high-speed rail network (HSRN), they rarely consider its service feature. Obviously, one of the key features of HSRN is offering passengers fast travel services. This study selects the passenger flow and travel time to describe the service feature of HSRN, and constructs a multiple-weight HSRN. Then, the service performance is taken as the indicator of HSRN's vulnerability by innovatively integrating complex network theory and reliability theory. Using the daily operation data of China's HSRN (CHSRN) merged with the coordinate data and statistics data in November 2018, this study assesses its vulnerability under natural hazards and manmade accidents. Afterwards, the critical component and critical area within CHSRN are identified by the spatially localized failures (SLFs) model. Comparing with the results obtained by previous methods, this study finds that the vulnerability of CHSRN is always overestimated, and some critical components are overlooked regardless of its service feature. Furthermore, we also find that the critical areas within CHSRN mostly lie on the Yangtze River Delta as considering the service feature, while they are scattered along Beijing-Guangzhou railway line or Beijing-Shanghai railway line as ignoring the feature. These findings can help administrators develop different strategies to better prioritize the allocation of limited maintenance resources and mitigate the vulnerability of HSRN.

Using Probabilistic Fault Tree Analysis and Monte Carlo Simulation to Examine the Likelihood of Risks Associated with Ballasted Railway Drainage Failure

Inadequate track drainage can lead to a variety of issues, including flooding, accelerated track degradation, and progressive or sudden failure of railway track, slope, or embankment. These can result in unplanned track maintenance, additional passenger travel costs, and damage to third party property. However, railway drainage asset management is challenging because it involves the consideration of large interconnected assets, limited maintenance budgets, and unknown failure probabilities. To address this issue, this paper introduces a risk-informed approach for railway drainage asset management that uses fault tree analysis to identify the factors that contribute to railway drainage flood risk and quantifies the likelihood of the occurrence of these factors using Monte Carlo simulation. This rational approach enables drainage asset managers to evaluate easily the factors that affect the likelihood of railway track drainage failure, thereby facilitating the prioritization of appropriate mitigation measures and in so doing improve the allocation of scarce maintenance resources. The analysis identified 46 basic and 49 intermediate contributing factors associated with drainage failure of ballasted railway track (undesired event). The usefulness of the approach is demonstrated for three sites on the UK railway network, namely, Ardsley Tunnel, Clay Cross Tunnel, and Draycott. The analysis shows that the Clay Cross Tunnel had the highest probability of drainage failure and should be prioritized for maintenance over the other two sites. The maintenance required should focus on blockages because of vegetation overgrowth or debris accumulation.

Optimal allocation model of maintenance resources for mobile pipeline pump unit

Oil transportation pump unit is an utterly important part of mobile pipeline, which provides power for oil. In war, the pump unit is easy to be damaged because of its large target. It is important of the quick and effective maintenance of pump unit to ensure the completion of oil transfer. With the non-exponential distribution of fault interval and maintenance time of oil transportation pump unit, an Arbitrary Distribution Stochastic Petri Net (ADSPN) is proposed, and an optimal allocation model of maintenance resources for mobile pipeline pump unit is established. Main functional subsystems of pumping unit are divided, parameters of fault interval time and maintenance time distribution of each subsystem are estimated based on pumping unit’s running data and thereby construct fault generation ADSPN model. Aiming at a certain oil transportation task, construct simulation model and simulate the task in different maintenance resources allocation plans. To meet the task requirement and make sure the resources’ utilization rate and maintenance personnel work intensity are also reasonable, optimal maintenance resources allocation plans are given. With the optimal maintenance resources allocation plans, oil transportation task can be completed in time and cost is decreased, illustrates the effectiveness and practicability of the proposed approach.