Minimum Maintenance Cost Research Articles

Techno-Economic Assessment of Voltage Sags Mitigation in Distribution System Connected to DGs

The penetration of DG in electrical distribution systems has many benefits. The paper highlights the impact of DG penetration at different load centers and different operating conditions in a distribution feeder. The impact of DG penetration on feeder voltage profile that depends on the size and location of DG in the system is illustrated. Mitigating methods are used to reduce the effect of voltage disturbances. Economic assessment studies are used for assisting in the economic choice of mitigating methods. Economical aspects of distribution systems connected to DGs are considered through the analysis of the life cycle cost of these distribution generation sources. Maintenance strategies are illustrated and discussed to keep these sources always ready for operation regarding minimization of maintenance cost. Technical and economical assessments depend on the availability of historical data for disturbances cost including damages and cost of disturbance mitigation using different mitigation methods. These data are difficult to be available for different types of loads. Therefore, load indices, K1, K2, and K3, are proposed in this paper for the different loads as referring it to reference load has available data to overcome the challenge of data shortage. Also, a techno-economic assessment index F is proposed for the assessment of the impact of mitigating device improving cost and its feasibility. PSCAD/EMTDC simulation software package is used in this analysis. The results show that the proposed techno-economic assessment methodology facilitates the decision of choosing the mitigation method for power quality problems and to overcome the challenge of data shortage in the techno-economic assessment.

Joint Maintenance Strategy Optimization for Railway Bogie Wheelset

A wheelset is one of the most severely worn components of railway bogies. Its health condition has a significant impact on the safety and comfort of railway trains. Moreover, wheelset maintenance costs account for a sizeable part of the railway operating company. Therefore, it is essential to investigate executable maintenance strategies for wheelsets. In this paper, a maintenance strategy that combines periodic inspection and preventive maintenance is proposed. The wheel flange and wheel diameter deterioration models are established by the compound Poisson process based on the cumulative shock from the failure mechanism, and the Weibull distribution is adopted for modeling wheel tread failure probability. The age reduction factor is introduced to describe the maintenance effect. Then, the joint maintenance optimization model is constructed to determine the periodic inspection interval and the reprofiling strategy, with the objective of achieving a minimum maintenance cost rate and with the wheel flange thickness failure as the failure risk constraint. Lastly, a case study is provided, and the results show that, compared with the two conventional maintenance strategies with fixed inspection periods, the maintenance strategy proposed in this paper can reduce the maintenance cost rate by 27.06% and 12.0%, respectively. Moreover, the life span is prolonged by 11.51% and 11.98%, respectively.

Development of cost-effective solar water desalination unit for the arid regions of rural areas of Sindh

In this study we have designed and fabricated a household solar water desalination unit for the arid regions of rural areas of Sindh like Tharparkar district. Drought conditions, shortage of drinking water and relying on saline water in arid regions of Sindh is a serious problem. In areas under these tracts, it is difficult to obtain drinkable water. This study focuses on the development of a cost effective and affordable desalination unit having simple construction, easy to use, repair, maintain and can be operated by an unskilled man. The solar desalination unit comprises of a solar oven, condenser, and collector pot. In this research various water samples were collected from three arid regions of Tharparkar district i.e., Islamkot, Diplo and Mithi. The performance of solar water desalination unit was assessed by analysing the Total Dissolve Solids (TDS), pH and Electrical Conductivity (EC) of all samples. The sample collected, before treatment had maximum TDS, conductivity, and pH values as 10421 mg/L, 20842 μS/cm and 8 respectively. Those sample after treatment reduced to TDS of 230 mg/L, conductivity of 460 μS/cm and pH of 7.4. This portable solar desalination unit showed remarkable results and found as effective in the removal of TDS from all water samples. The efficiency of unit was calculated as 35.2% with maximum daily output of 5 litres. This unit was constructed at very low cost of PAKPKR 4500/- and its complete rely on solar energy which makes it quite affordable for those which cannot afford expensive treatment techniques. This unit has very minimal maintenance cost and does not require electricity. The values for pH, TDS, and EC achieved from this unit are satisfying the World Health Organization (WHO) guidelines for drinking water quality.

The Challenges in online Teaching-Learning process

The worldwide irruption of COVID-19 pandemic has jeopardized the all the sectors of our society. Education System has been disrupted hugely affecting students, teachers of school colleges and universities. Online education system emerged as the only possible solution. But nationwide online teaching-learning process brings huge challenges due to the inadequacies and inequities of our education system. availability of smooth internet connection, access to required gadgets, being familiar and getting adapted to the online environment, maintaining privacy are parts of the challenge faced by almost all the academic staffs. Students feel isolated, lose their concentration and desire for learning, and their attention span decreases. Physical and mental health of the children, students can also get hampered due to excess screen time and absence of bodily movements due to continuous online classes. Not everyone can bear the cost of technological tools needed for online education. Many teachers and parents, not being familiar and comfortable with the technological tools, find it difficult to guide their students and children respectively. Planning and strategizing scenarios for the worst possible conditions could be the key. These difficult times have also taught us that in order to survive the crisis, students must be capable of problem-solving, critical thinking, and, most importantly, adaptability. Efficient technology that is able to effectively facilitate educational processes but has minimum maintenance and procurement costs, should be brought in after their pros and cons are thoroughly weighed.

Multi-Objective Optimal Planning for Distribution Network Considering the Uncertainty of PV Power and Line-Switch State.

With the construction of the smart grid, the distribution network with high penetration of the photovoltaic (PV) generator relies more and more on cyber systems to achieve active control; thus, the uncertainty of PV power and the line-switch state will inevitably affect the distribution network. To avoid the situation, a min–max multi-objective two-level planning model is proposed. Firstly, the uncertainty of PV power is considered, and a multi-time PV power model is established. Followed by the analysis of the line-switch state uncertainty in the distribution network, and according to Claude Shannon’s information theory, the line-switch state uncertainty model is established under multiple scenarios. After the distribution network reconfiguration, the Latin hypercube sampling (LHS) method is used to determine the line-switch state when the uncertainty budget is different. Finally, considering the worstcase by the uncertainty of PV power and line-switch status, the control model is proposed to improve the stability of the distribution network with the minimal maintenance cost. The model feasibility is verified by the test system and the characteristics of PV power uncertainty, the line-switch state uncertainty is analyzed, and the influence of the scheduling strategy is discussed, thus providing practical technical support for the distribution network.

The importance of fuzzy logic integrated to inventory management: case study in a company x in the industrial district of manaus

Inventory management is a practice of fundamental importance for the success of companies in the most varied segments. Companies that operate with low levels of profitability depend on efficient inventory management, under penalty of losing competitiveness. Efficient inventory maintenance is not characterized by the existence of large batches of products for prompt customer service or, on the contrary, by the lack of inventories in an attempt to minimize maintenance costs. Inventories must be managed in a balanced way to ensure an adequate level of service to customers and generate profits. This work aimed to develop a Fuzzy model for stock management of an organization, to reduce the lack of material in stock, resulting from the "lead time". The work is justified by the importance of having a management monitoring the control of inventory costs, which includes the expenses resulting from the lack of items, the costs related to the replacement of the stock, and the expenses of a stopped stock. To accomplish this, it was necessary to map the Material Purchasing Process and thus identify which were the most significant variables in inventory management. The use of Fuzzy Logic, idealized for a model like this, is to reach an inventory level close to zero, immediately before physical replacement, thus minimizing unnecessary costs. Therefore, experimental research aims to manipulate and control the research variables. The application of the developed model shows that the evaluation of Inventory Management through the Need for Replacement of Input and Type of Transport to be used through the fuzzy methodology proved to be viable to assist the desired observations, allowing to understand the impact of each linguistic variable in the research result, pointing out how company X can use the information obtained to improve the structure of its Logistics Process, thus reducing the lack of material in stock due to the lead time.

Evolutionary Swarm based Optimization Algorithm for Power Loss Minimization in Distributed Generation System

The electrical energy requirement is increasing day by day as many of the mechanical systems like motor vehicles and industrial engines are compensated with electrical equipment due to its environment friendly nature. However, most of the design of electrical power generation units do not seem to be eco-friendly as that of the electrical motors and drives. Therefore, the concentration has moved towards the non-renewable energy generation possibilities. Power stations that are operated by wind, solar and hydro stations attract the developers due to their minimum maintenance cost and higher operational efficiency. Hence, the combinations of two or three renewable energy sources are clubbed in many places to form a Distributed Generation (DG) setup. Such DG system requires an efficient switching operation for managing the power outcome from different generating stations to meet the load requirement. To meetup such requirement, a combination of Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) based technique has been developed in this work. A simulated experiment is also conducted in the work to prove the efficiency of the proposed hybrid model over the traditional GA and PSO approaches.

A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions

In industry, electric motors such as the squirrel cage induction motor (SCIM) generate motive power and are particularly popular due to their low acquisition cost, strength, and robustness. Along with these benefits, they have minimal maintenance costs and can run for extended periods before requiring repair and/or maintenance. Early fault detection in SCIMs, especially at low-load conditions, further helps minimize maintenance costs and mitigate abrupt equipment failure when loading is increased. Recent research on these devices is focused on fault/failure diagnostics with the aim of reducing downtime, minimizing costs, and increasing utility and productivity. Data-driven predictive maintenance offers a reliable avenue for intelligent monitoring whereby signals generated by the equipment are harnessed for fault detection and isolation (FDI). Particularly, motor current signature analysis (MCSA) provides a reliable avenue for extracting and/or exploiting discriminant information from signals for FDI and/or fault diagnosis. This study presents a fault diagnostic framework that exploits underlying spectral characteristics following MCSA and intelligent classification for fault diagnosis based on extracted spectral features. Results show that the extracted features reflect induction motor fault conditions with significant diagnostic performance (minimal false alarm rate) from intelligent models, out of which the random forest (RF) classifier was the most accurate, with an accuracy of 79.25%. Further assessment of the models showed that RF had the highest computational cost of 3.66 s, while NBC had the lowest at 0.003 s. Other significant empirical assessments were conducted, and the results support the validity of the proposed FDI technique.

Availability and Maintenance Analysis for Multi-Failure System Modeled by Alternating Renewal Processes Subject to M/E2/1 Queue

In this paper, it is aimed to propose a notion of the alternating renewal process to mathematically model a multi-failure complex system in order to develop its availability and maintenance measures. In the study, M/E2/1 queueing model with infinite waiting space during service times of components has been worked out under the FCFS discipline. Inspections are performed at regular intervals to detect breakdowns. The primary objective of the paper is about obtaining the system’s reliability, availability, and the optimal interval period with minimum maintenance cost. To validate the mentioned model a case study has been done on the Power Distribution System. Finally, a comparative study between M/E2/1 and M/M/1 queuing models is done and the various aspects based on availability and long-run cost are focused in the analytical result. The results were direct toward enhancing the availability of the system and making the decision about an appropriate inspection period.

Simulation for predictive maintenance using weighted training algorithms in machine learning

<span>In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decision-making for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artificial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classification, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.</span>

An approach for bi-objective maintenance scheduling on a networked system with limited resources

Preventive maintenance activities are often the cause of downtime of technical multi-component systems. To minimize maintenance costs and maximize productivity, maintenance tasks are often grouped and carried out simultaneously. We consider the problem of obtaining an optimal maintenance schedule when the multi-component system is also a networked system and can be modeled as a directed graph, where nodes represent machines or workers, and edges represent the exchange of material, information, or work between these nodes. To find efficient maintenance schedules, we formulate a bi-objective optimization problem, which considers the limited availability of maintenance personnel, and we propose an algorithm that finds a set of maintenance schedules, which are a good approximation of the Pareto front in terms of costs and productivity. Through sensitivity analysis we show the extent to which adding maintenance personnel improves system productivity at the expense of increased maintenance costs and idle time of some resources. Besides solving the Pareto-optimal schedules, we show how the developed model is useful in maintenance personnel planning, and we outline limitations and future developments of the present work.

State of Health estimation and Remaining Useful Life prediction for lithium-ion batteries by Improved Particle Swarm Optimization-Back Propagation Neural Network

Accurate State of Health (SOH) estimation and Remaining Useful Life (RUL) prediction play important roles in ensuring the safe operation of the batteries and minimizing maintenance costs. It is difficult to directly measure the SOH and RUL of batteries in actual application. This paper estimates SOH and predicts RUL based on Improved Particle Swarm Optimization-Back Propagation Neural Network (IPSO-BPNN) with Health Indicators (HIs) as input. The HIs are extracted from the lithium-ion batteries charging process because the charging process is stable and easy to measure. Aiming at the nonlinear problem of batteries, Back Propagation Neural Network (BPNN) with strong generalization ability is used to estimate SOH and predict RUL. In order to solve the problem of BPNN parameter initialization, Particle Swarm Optimization (PSO) combined with variation factor is adopted in this paper to optimize the initial weights and thresholds of the neural network. In addition, the exponential decaying learning rate is adopted to improve the stability and learning efficiency of the network. Two datasets of batteries are used to verify the proposed IPSO-BPNN method. The results show that compared with the standard BPNN method, the maximum root mean square error and mean absolute error of SOH estimation results by the proposed IPSO-BPNN method are reduced to 0.78% and 1.01% respectively, which proves IPSO-BPNN method has higher accuracy and validity than standard BPNN method.

Light-weight federated learning-based anomaly detection for time-series data in industrial control systems

With the emergence of the Industrial Internet of Things (IIoT), potential threats to smart manufacturing systems are increasingly becoming challenging, causing severe damage to production operations and vital industrial assets, even sensitive information. Hence, detecting irregularities for time-series data in industrial control systems that should operate continually is critical, ensuring security and minimizing maintenance costs. In this study, with the hybrid design of Federated learning, Autoencoder, Transformer, and Fourier mixing sublayer, we propose a robust distributed anomaly detection architecture that works more accurately than several most recent anomaly detection solutions within the ICS contexts, whilst being fast learning in minute time scale. This distributed architecture is also proven to achieve lightweight, consume little CPU and memory usage, have low communication costs in terms of bandwidth consumption, which makes it feasible to be deployed on top of edge devices with limited computing capacity.

A two-stage stochastic programming model for selective maintenance optimization

The problem of selective maintenance exists in many multicomponent systems carrying out an alternating sequence of missions, with scheduled breaks where only a limited number of components can be maintained due to time limit. In this paper, a stochastic programming approach is proposed for determining an optimum maintenance plan to minimize maintenance costs and expected failure costs, while maximizing the probability of successful accomplishment of the next mission under uncertainties in future operating conditions. Traditionally, future operating conditions that affect failure time distribution when calculating reliability in selective maintenance models were assumed as deterministic. In this study, future operating conditions are assumed to be uncertain. The system is subject to several uncertain condition scenarios of exposure, conditional, usage, stress, etc. Each scenario is modeled with its associated occurrence probability. The presented model is a two-stage stochastic mixed-integer nonlinear programming model with fixed recourse, where the first stage is associated with maintenance decisions made before uncertainties are revealed, and the second stage is modeled as a recourse function which is related to the occurrence probability of system failure. A numerical example of a series-parallel system is used to demonstrate the effectiveness of the suggested model.

Reliability and hybrid maintenance modeling for competing failure systems with multistage periods

This paper deals with the reliability and hybrid maintenance models for systems which are subjected to competing and dependent failures due to degradation and external shocks. System studied here are with multistage periods, standby stage, working stage and the stop stage, which makes it different with previous literatures. The characters of degradation and external shocks vary with different periods and this also makes the proposed reliability model more challenging and more consistent with the engineering reality. Besides, a hybrid maintenance policy combined by age-based preventive maintenance and condition-based maintenance is studied by minimizing the average long-run maintenance cost. The optimal age-based preventive interval and the condition-based threshold are both calculated to manage the minimum maintenance cost. A numerical case demonstrates the availability and general applicability of proposed reliability and maintenance models at the end of this paper.

Dampak Penambahan Campuran Aspal Penetrasi 60/70 Sebanyak 3% pada 1 Lapisan dan 3 Lapisan Struktur Balas

The addition of asphalt material is expected to increase service life and minimize maintenance costs from conventional ballast structures. The purpose of this study was to determine the weight of test material + asphalt 3%, compressive strength, vertical deformation, elastic modulus, abrasion value and damage to the test specimen after loading. The method used was the compressive test method while for the test object used consists of dirty ballast, clean ballast, dirty ballast + 3% asph alt 1 layer, dirty ballast + 3% asphalt 3 layers, clean ballast + 3% asphalt 1 layer, clean ballast + 3% asphalt 3 layers. The test objects were modeled with a cube measuring 0.4 x 0.3 x 0.2 m. The test results show that the addition of asphalt can increase the deformation value and can reduce the modulus of elasticity. However the presence of asphalt as a binding material can minimize the valueof abrasion and damage to the reciprocating constituent material.

Offline and Online Measurement of the Geometries of Train Wheelsets: A Review

Train wheelset is one of the most important and costly-on-maintenance components in the rolling stock. Timely assessment of the wheelset wear can prevent the wheelset wear induced derailment, as well as enable the scheduling of optimal maintenance strategy, like when to perform reprofiling or when to replace, to maximize availability, minimize downtime, and minimize maintenance costs. The geometric parameters of wheelset that need to be constantly measured include wheel diameter and wheel profile. Various measurement methods, from the classical handheld gauges to machine vision-based sophisticated online noncontact vision systems, have emerged for the timely measurement of the geometrics of train wheelsets. Despite the significant development on train wheelset geometrics measurement methods in the past decades, a systematic review is not available yet. In this paper, we systematically review reported gauges and systems for measuring the geometrics of train wheelsets. Forty-six research articles and 13 granted patents published from 1990 to 2022 were reviewed. We provide a categorization of the measurement methods, and then detailly reported the gauges and systems based on offline and online categorization. At the end, the limitations of the state-of-the-art and the research prospects are given.

Measurements Result Analysis of Deformation Characteristics of Transition Zones on the Modernized Line Púchov – Považská Teplá

Abstract The current trend in Europe and worldwide is to support the development of so-called green transport, characterized by lower energy consumption and lower CO2 emissions. Rail transport meets these conditions and therefore considerable funding is being spent not only on the construction of high-speed lines but also on the modernization of existing railways, especially those forming part of the trans-European corridors. Three corridors of the basic TEN-T network pass through the territory of the Slovak Republic. In the Žilina Region, there are two corridors, Balt – Adriatic and Rhine – Danube, with a common section Púchov – Žilina, which is currently being modernized. It is a known fact that railway lines often show a greater degree of degradation in the transition zones between the earthwork and the structures of sub-ballast layers. Since every railway infrastructure management aims to minimize maintenance costs, while ensuring the safety and comfort of passengers, it is necessary to pay increased attention also to transiton zones. In this context, the presented paper deals with the analysis of values of deformation characteristics on the modernized line section Púchov – Považská Teplá, which is characterized by several bridge structures and tunnels. The values were obtained within the quality inspection of work in transition zones located between the earthwork and the structures of sub-ballast layers.

Factors affecting maintenance cost of public buildings: case study of Tanzania Buildings Agency and National Housing Corporation

PurposeThe state of public buildings in Tanzania exhibits evidence of a lack of maintenance and repair. Despite efforts to raise the performance of public buildings, little emphasis is placed on maintenance works and related factors affecting maintenance costs of public buildings, which are often high. This study aims to examine factors affecting maintenance costs of public buildings.Design/methodology/approachThis was a case study involving two government building agencies – Tanzania Buildings Agency (TBA) and National Housing Corporation (NHC). A total of 387 tenants and 29 experts participated in the study. Data were collected using questionnaires and analyzed using Stata version 16 – descriptive statistics feature reporting absolute numbers and frequencies.FindingsBoth tenants and experts ranked building age, poor quality control, building material used, execution of work when it is urgent and inadequate financial resources as major factors affecting maintenance costs of public buildings. In the distinctive rating, tenants ranked resource management as a major factor, while experts ranked ignorance about the property of materials, misuse of buildings and faulty maintenance as major factors.Research limitations/implicationsThe study has some limitations such as geographical coverage which is limited to Dar es Salaam and Dodoma regions, hence missing information from other regions may have different experiences. Additionally, data were collected in a self-reported manner which could bring about information bias.Practical implicationsFindings of this study provide an insight on recent developments on the factors affecting maintenance cost of buildings using two cases of NHC and TBA which are single entities owning a substantial amount of housing units in Tanzania. Factors affecting the maintenance cost of public buildings identified in this study is a threshold for experts to devise strategies for minimizing maintenance costs.Originality/valueTo the best of the authors’ knowledge, this is the first study that assessed factors affecting maintenance cost in public buildings run by TBA and NHC from the tenant and expert perspectives.

Maintenance cost minimization models for offshore wind farms: A systematic and critical review

Feasible and sustainable source of power is a burning question nowadays. The offshore wind farms can be a solution to power generation problem, but it is relatively more expensive. Its installation cost is more than twice of its similar size onshore counterpart. The cost that matters most is the operations and maintenance (O&M) cost. Expensive and sophisticated transfer vehicles as well as highly skilled technicians are needed to conduct O&M activities, which results in a remarkably higher O&M cost for an offshore wind farm project. Few researchers proposed some strategies that could resolve the problem nicely, although there is no universal maintenance strategy, which will fit all the conditions. Optimal maintenance strategy depends on a lot of factors such as cost of energy, level of reliability needed, weather conditions, availability of skilled technicians, availability of crew transfer vehicles, and to mention a few. Total 190 research articles related to offshore maintenance have been reviewed, and some prominent models have been discussed in detail. Some risk and reliability-based models reduced annual maintenance cost by 23%, whereas some other opportunistic maintenance strategy was able to minimize 32% of production loss and transportation cost. Compatibility and usability of different models and results are highlighted. This critical review is aimed at identifying the relevant research outcomes and comparing their critical aspects to provide a good guideline about optimal maintenance strategy to the maintenance managers.