Minimum Maintenance Cost Research Articles

Feed-Forward Neural Network Based Petroleum Wells Equipment Failure Prediction

In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model’s primary goal is to identify potential malfunctions at an early stage to ensure the production process’ continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.

Data-driven predictive maintenance method for digital welding machines

Digital welding machine (DWM) is an advanced tool for material forming. The lifespan and health status of DWMs are closely related to the safety and reliability. To address the problem of low accuracy in the lifespan prediction of DWMs, a model based on immune algorithm (IA) and long short-term memory network (LSTM) with attention mechanism is proposed. First, the degradation characteristic indicators of the lifespan of DWMs are evaluated and selected. Then, a health index is constructed using linear regression to quantitatively reflect the lifespan status of DWMs. The optimized model is used to predict the remaining lifespan, and compared with various models using 5 indicators. Finally, predictive maintenance of DWMs is carried out based on product inspection and production scheduling. the optimal solution for the objective function is obtained to calculate the best predictive maintenance method for the digital welding machine.During the lifespan prediction process, the optimized model has a 20% decrease in root mean square error and a 35.8% decrease in mean square error compared to the traditional LSTM model. The average absolute error is decreased by 14.2% and the average absolute percentage error is closer to 0, while the coefficient of determination increases by 23%. By combining with actual production line arrangements, maintenance of DWMs can be performed at the most appropriate time to minimize maintenance costs.

DESIGN AND FABRICATION OF VARIABLE SPEED ABRASIVE GRINDING AND POLISHING MACHINE FOR METALLOGRAPHIC SAMPLE PREPARATION

The polishing machine is one of the most useful pieces of metallography equipment for the grinding and polishing of metallic materials, particularly, surfaces of specimens. This is required in order to produce a flat, smooth and mirror-like surface before determine their physical structure using microscopy. This paper reports the design and construction of a variable speed abrasive polishing machine for physical metallographic applications. The machine design and construction are principled on using locally available materials for its fabrication. The machine consists of an electrical powered motor, pulleys, belt, rotating shaft, light aluminium alloy polishing disk plate, bush bearing, pillow bearings enclosed in metal casing. The test-running and evaluation of the machine was carried out at different speeds, grits of abrasive papers and on variety of materials (metal alloys, composites, plastic, ceramics) for both dry and wet operating conditions. Testing the machine was done with and without specimen on the rotating table for grinding and polishing. The machine is simple and safe to operate and requires minimum maintenance cost. The specimen size should not be less than 25mm diameter by 10mm thickness for easy hand held polishing operation.

Cotton yield prediction using drone derived LAI and chlorophyll content

The unmanned aerial vehicles (UAV) have become a better solution for agricultural growers due to advanced features such as minimal maintenance costs, quick set-up time, low acquisition costs, and live data capturing. Near-ground remote sensing (drone) has opened up new agronomic opportunities for better crop management. This study predicted the seed cotton yield for a cotton field area located at Tamil Nadu Agricultural University, Coimbatore. Pearson correlation analysis and regression analysis were done for ground truth data and vegetation indices for validation and accuracy and also to find the best-performing indices. It was concluded that the Wide Dynamic Range Vegetation Index (WDRVI) showed a better correlation coefficient (R=0.959) with LAI ground truth data (R2=0.919). In contrast, the Modified Chlorophyll Absorption Ratio Index (MCARI) showed a better correlation coefficient (R=0.919) with SPAD chlorophyll ground truth data (R2=0.845). Then the best performing indices WDRVI and MCARI were further used for generating the yield model. High spatial resolution drone imageries for determining LAI and chlorophyll are reliable and rapid, as per the study. It helps to determine the LAI and chlorophyll at a spatial scale and their influence on yield production. This yield prediction was technical support for the widespread adoption and application of unmanned aerial vehicle (UAV) remote sensing in large-scale precision agriculture.

Site Assessment Instrument for Regional Maintenance Center

To minimize maintenance cost and improve rural transit vehicles services, a Regional Maintenance Center (RMC) concept is being considered by Texas. Currently, rural transit vehicles are maintained and repaired by local garages, where service fees and quality of work performed often are questionable. RMCs are designed to maintain and repair rural transit vehicles within a geographical region. A cost-efficient method to create an RMC is by upgrading an existing maintenance operation. The objective of this study is to create a site assessment instrument to assist in the process of selecting potential maintenance operations that could be upgraded to an RMC. Upon interviewing various rural transportation experts and visiting the benchmark RMC in Illinois, a list of criteria crucial for a successful RMC was compiled and classified into various categories. The result of this benchmarking was used in a preliminary study of Lubbock County, Texas, and vicinity.

Influence of vehicle traffic on modal-based bridge monitoring

In recent years, there has been a surge of interest in modal-based monitoring systems, especially for bridge structures, to detect damage at an early stage and to extend the life of existing bridges as well as to minimize maintenance costs. The modal parameters, natural frequencies and mode shapes, depend on stiffness and mass of a structure. For an economic application of a modal-based monitoring system, the estimation of the modal parameters based on the Operational Modal Analysis needs to be estimated. The Operational Modal Analysis uses the naturally existing vibration sources, such as wind and traffic, to estimate the modal parameters. A significant advantage is therefore that the bridges do not have to be closed to traffic. In this context, the modal parameters are influenced by the vehicle traffic due to the associated temporally and spatially variable mass, stiffness and damping properties of the overall system. In this paper, the influence of those operational loads and road roughness on the modal parameter estimation is numerically investigated. The vehicles are modeled as half-vehicle models. In addition, the influence of vehicle traffic is shown using in situ measurements on an existing bridge. A high sensitivity of the natural frequencies to operational loads can be shown, so that they are not reliable damage identification parameters. In contrast, the mode shapes exhibit small amplitude scatter due to the operating loads if the excitation frequency is not in the range of the natural frequency of the bridge. This is very unlikely, especially for bridges under vehicle traffic, due to the variety of vehicle designs and thus the broadband excitation.

A bidding mechanism for maintenance of generation units considering inter-ISO power exchange

To ensure the reliability of power systems, the independent system operator (ISO) manages the planning process of the maintenance of generation units for generation companies (GENCOs). This paper focuses on a widely studied two-layer long-term predictive maintenance decision making framework in a deregulated environment. In the first layer the ISO-wide maintenance schedule is optimized for the GENCOs, targeting minimal total maintenance cost and degradation statuses. In the second layer, a bidding mechanism is designed for GENCOs who are not satisfied with the time slots scheduled by the first layer, so that they can bid for their preferred time slots. A novel bidding mechanism for the host ISO (i.e., the ISO that manages the maintenance planning process) is proposed, called interchangeable bidding mechanism for maintenance (IBMM). In this mechanism, the GENCOs of the host ISO can use their bid price to purchase the supportive energy from the GENCOs of the neighbor ISOs. Furthermore, they also can pay a penalty fee for reducing the amount of energy transmitted from the host ISO to the neighbor ISO with respect to what has been stipulated in the long-term inter-ISO power exchange contract. IBMM provides more opportunities for GENCOs of the host ISO to obtain their preferred maintenance time slots. Additionally, the power system reliability can be ensured. IBMM is formulated as a mixed-integer non-linear bidding programming problem. Then, the bidding programming problem is recast into a mixed-integer second-order cone programming (MISOCP) problem that can be solved using Gurobi. In the case study, the IEEE 118-bus network is studied to illustrate the performance of the proposed bidding strategy.

Laying it on thick: Ecosystem effects of sediment placement on a microtidal Rhode Island salt marsh.

Heightened recognition of impacts to coastal salt marshes from sea-level rise has led to expanding interest in using thin-layer sediment placement (TLP) as an adaptation tool to enhance future marsh resilience. Building on successes and lessons learned from the Gulf and southeast U.S. coasts, projects are now underway in other regions, including New England where the effects of TLP on marsh ecosystems and processes are less clear. In this study, we report on early responses of a drowning, microtidal Rhode Island marsh (Ninigret Marsh, Charlestown, RI) to the application of a thick (10-48 cm) application of sandy dredged material and complimentary extensive adaptive management to quickly build elevation capital and enhance declining high marsh plant species. Physical changes occurred quickly. Elevation capital, rates of marsh elevation gain, and soil drainage all increased, while surface inundation, die-off areas, and surface ponding were greatly reduced. Much of the marsh revegetated within a few years, exhibiting aspects of classic successional processes leading to new expansive areas of high marsh species, although low marsh Spartina alterniflora recovered more slowly. Faunal communities, including nekton and birds, were largely unaffected by sediment placement. Overall, sediment placement provided Ninigret Marsh with an estimated 67-320 years of ambient elevation gain, increasing its resilience and likely long-term persistence. Project stakeholders intentionally aimed for the upper end of high marsh plant elevation growth ranges to build elevation capital and minimize maintenance costs, which also resulted in new migration corridors, providing pathways for future marsh expansion.

Exploiting dynamic programming in optimizing reliability-centered maintenance

Maintenance Scheduling (MS) is one of the most persistent issues that might arise in a manufacturing facility. It is crucial to do routine maintenance on machinery in order to avoid unplanned breakdowns. This type of failure could cause costly manufacturing process interruptions. Numerous strategies have been developed in an effort to deal with MS. But because each system has specific requirements and limitations, this is a particularly challenging problem. The scheduling of maintenance for machine units at a manufacturing facility that produces aluminum in moderate amounts is explored in this study using a dynamic programming approach. The Maintenance Scheduling model is put into practice using a Reliability-Centered Maintenance (RCM) strategy after an investigation of the architecture and infrastructure of the plant. By maintaining machines at acceptable dependability values and minimizing maintenance costs, this method is created to optimize the maintenance schedule. Here, factors like reliability and failure rate that affect the MS problem are discussed and investigated. Applying the model to a situation that represents the aluminum manufacturing allows it to be tested in a variety of different circumstances. The results of applying the model to the test cases are given, followed by a discussion of the results. The results obtained are reasonable and show that the dynamic programming strategy is a successful way to fix the MS issue that the manufacturing plant's machines are experiencing.

Combined Use of GPR and Other NDTs for Road Pavement Assessment: An Overview

Roads are the main transportation system in any country and, therefore, must be maintained in good physical condition to provide a safe and seamless flow to transport people and goods. However, road pavements are subjected to various defects because of construction errors, aging, environmental conditions, changing traffic load, and poor maintenance. Regular inspections are therefore recommended to ensure serviceability and minimize maintenance costs. Ground-penetrating radar (GPR) is a non-destructive testing (NDT) technique widely used to inspect the subsurface condition of road pavements. Furthermore, the integral use of NDTs has received more attention in recent years since it provides a more comprehensive and reliable assessment of the road network. Accordingly, GPR has been integrated with complementary NDTs to extend its capabilities and to detect potential pavement surface and subsurface distresses and features. In this paper, the non-destructive methods commonly combined with GPR to monitor both flexible and rigid pavements are briefly described. In addition, published work combining GPR with other NDT methods is reviewed, emphasizing the main findings and limitations of the most practical combination methods. Further, challenges, trends, and future perspectives of the reviewed combination works are highlighted, including the use of intelligent data analysis.

Grid-connected hybrid PV-wind system simulation in urban Java

This research proposes optimizing the energy harvesting system based on solar and wind energy, using the enhanced PV-Wind Turbine hybrid power grid to supply electrical energy somewhere in the city of Yogyakarta, Indonesia. The analysis was conducted by researching the possibilities of wind and solar energy and gathering data from numerous sources. To examine the available data and the economic feasibility of the proposed hybrid power system, the Hybrid Optimization Model for Electric Renewable (HOMER) program was employed. The scheme was simulated and optimized using the National Renewable Energy Laboratory's Hybrid Optimization System for Electric Renewables model (NREL). Various analyses are examined, such as Net Present Value (NPV), Energy Expenditure, Energy Output, Usage, and Excess Energy created by each component of the resultant system. The results gained via the installation of this hybrid system are fairly excellent due to monthly production optimization and minimal maintenance costs with potential investments.

Squat Detection of Railway Switches and Crossings Using Wavelets and Isolation Forest.

Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects.

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.