Search results for: model maintenance

Authors: Qingqing Feng, S. Thomas Ng, Frank J. Xu, Jiduo Xing

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Faced with unprecedented challenges including aging assets, lack of maintenance budget, overtaxed and inefficient usage, and outcry for better service quality from the society, today’s infrastructure systems has become the main focus of many metropolises to pursue sustainable urban development and improve resilience. Digital twin, being one of the most innovative enabling technologies nowadays, may open up new ways for tackling various infrastructure asset management (IAM) problems. Digital twin application for IAM, as its name indicated, represents an evolving digital model of intended infrastructure that possesses functions including real-time monitoring; what-if events simulation; and scheduling, maintenance, and management optimization based on technologies like IoT, big data and AI. Up to now, there are already vast quantities of global initiatives of digital twin applications like 'Virtual Singapore' and 'Digital Built Britain'. With digital twin technology permeating the IAM field progressively, it is necessary to consider the maturity of the application and how those institutional or industrial digital twin application processes will evolve in future. In order to deal with the gap of lacking such kind of benchmark, a draft maturity model is developed for digital twin application in the IAM field. Firstly, an overview of current smart cities maturity models is given, based on which the draft Maturity Model of Digital Twin Application for Infrastructure Asset Management (MM-DTIAM) is developed for multi-stakeholders to evaluate and derive informed decision. The process of development follows a systematic approach with four major procedures, namely scoping, designing, populating and testing. Through in-depth literature review, interview and focus group meeting, the key domain areas are populated, defined and iteratively tuned. Finally, the case study of several digital twin projects is conducted for self-verification. The findings of the research reveal that: (i) the developed maturity model outlines five maturing levels leading to an optimised digital twin application from the aspects of strategic intent, data, technology, governance, and stakeholders’ engagement; (ii) based on the case study, levels 1 to 3 are already partially implemented in some initiatives while level 4 is on the way; and (iii) more practices are still needed to refine the draft to be mutually exclusive and collectively exhaustive in key domain areas.

Keywords: digital twin, infrastructure asset management, maturity model, smart city

Procedia PDF Downloads 123

Authors: Soumava Boral, Sanjay K. Chaturvedi, Ian Howard, Kristoffer McKee, V. N. A. Naikan

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Due to strict regulations from government to enhance the possibilities of sustainability practices in industries, and noting the advances in sustainable manufacturing practices, it is necessary that the associated processes are also sustainable. Maintenance of large scale and complex machines is a pivotal task to maintain the uninterrupted flow of manufacturing processes. Appropriate maintenance practices can prolong the lifetime of machines, and prevent associated breakdowns, which subsequently reduces different cost heads. Selection of the best maintenance strategies for such machines are considered as a burdensome task, as they require the consideration of multiple technical criteria, complex mathematical calculations, previous fault data, maintenance records, etc. In the era of the fourth industrial revolution, organizations are rapidly changing their way of business, and they are giving their utmost importance to sensor technologies, artificial intelligence, data analytics, automations, etc. In this work, the effectiveness of several maintenance strategies (e.g., preventive, failure-based, reliability centered, condition based, total productive maintenance, etc.) related to a large scale and complex gearbox, operating in a steel processing plant is evaluated in terms of economic, social, environmental and technical criteria. As it is not possible to obtain/describe some criteria by exact numerical values, these criteria are evaluated linguistically by cross-functional experts. Fuzzy sets are potential soft-computing technique, which has been useful to deal with linguistic data and to provide inferences in many complex situations. To prioritize different maintenance practices based on the identified sustainable criteria, multi-criteria decision making (MCDM) approaches can be considered as potential tools. Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) is a recent addition in the MCDM family and has proven its superiority over some well-known MCDM approaches, like TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ELECTRE (ELimination Et Choix Traduisant la REalité). It has a simple but robust mathematical approach, which is easy to comprehend. On the other side, due to some inherent drawbacks of Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS), recently, the use of Fermatean Fuzzy Sets (FFSs) has been proposed. In this work, we propose the novel concept of FF-MAIRCA. We obtain the weights of the criteria by experts’ evaluation and use them to prioritize the different maintenance practices according to their suitability by FF-MAIRCA approach. Finally, a sensitivity analysis is carried out to highlight the robustness of the approach.

Keywords: Fermatean fuzzy sets, Fermatean fuzzy MAIRCA, maintenance strategy selection, sustainable manufacturing, MCDM

Procedia PDF Downloads 115

Authors: Abdullah Eqal Al Mazrooei

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In this work, a new nonlinear model will be introduced. The model is in the state-space form. The nonlinearity of this model is in the state equation where the state vector is multiplied by its self. This technique makes our model generalizes many famous models as Lotka-Volterra model and Lorenz model which have many applications in the real life. We will apply our new model to estimate the wind speed by using a new nonlinear estimator which suitable to work with our model.

Keywords: nonlinear systems, state-space model, Kronecker product, nonlinear estimator

Procedia PDF Downloads 662

Authors: Matias Micheletto, Rodrigo Santos, Sergio F. Ochoa

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In developing countries, the most roads in rural areas are dirt road. They require frequent maintenance since are affected by erosive events, such as rain or wind, and the transit of heavy-weight trucks and machinery. Early detection of damages on the road condition is a key aspect, since it allows to reduce the main-tenance time and cost, and also the limitations for other vehicles to travel through. Most proposals that help address this problem require the explicit participation of drivers, a permanent internet connection, or important instrumentation in vehicles or roads. These constraints limit the suitability of these proposals when applied into developing regions, like in Latin America. This paper proposes an alternative method, based on unattended crowdsensing, to determine the quality of dirt roads in rural areas. This method involves the use of a mobile application that complements the road condition surveys carried out by organizations in charge of the road network maintenance, giving them early warnings about road areas that could be requiring maintenance. Drivers can also take advantage of the early warnings while they move through these roads. The method was evaluated using information from a public dataset. Although they are preliminary, the results indicate the proposal is potentially suitable to provide awareness about dirt roads condition to drivers, transportation authority and road maintenance companies.

Keywords: dirt roads automatic quality assessment, collaborative system, unattended crowdsensing method, roads quality awareness provision

Procedia PDF Downloads 179

Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Jessica Achebe, Susan Tighe

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The reduction of material consumption and greenhouse gas emission when maintain and rehabilitating road networks can achieve added benefits including improved life cycle performance of pavements, reduced climate change impacts and human health effect due to less air pollution, improved productivity due to an optimal allocation of resources and reduced road user cost. This is the essence of incorporating environmental sustainability into pavement management. The functionality of performance measurement approach has made it one of the most valuable tool to Pavement Management Systems (PMSs) to account for different criteria in the decision-making process. However measuring the environmental performance of road network is still a far-fetched practice in road network management, more so an ostensive agency-wide environmental sustainability or sustainable maintenance specifications is missing. To address this challenge, this present research focuses on the environmental sustainability performance of network-level pavement management. The ultimate goal is to develop a framework to incorporate environmental sustainability in pavement management systems for network-level maintenance programming. In order to achieve this goal, this paper present the first step, the intention is to review the previous studies that employed environmental performance measures, as well as the suitability of environmental performance indicators for the evaluation of the sustainability of network-level pavement maintenance strategies. Through an industry practice survey, this paper provides a brief forward regarding the pavement manager motivations and barriers to making more sustainable decisions, and data needed to support the network-level environmental sustainability. The trends in network-level sustainable pavement management are also presented, existing gaps are highlighted, and ideas are proposed for network-level sustainable maintenance and rehabilitation programming.

Keywords: pavement management, environment sustainability, network-level evaluation, performance measures

Procedia PDF Downloads 277

Authors: Shankha Pratim Bhattacharya

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In general building repair and renovation projects are minor in nature. It requires less attention as the primary cost involvement is relatively small. Although the building repair and maintenance projects look simple, it involves much complexity during execution. Many of the present research indicate that few uncertain situations are usually linked with maintenance projects. Those may not be read properly in the planning stage of the projects, and finally, lead to time overrun. Building repair and maintenance become essential and periodical after commissioning of the building. In Institutional buildings, the regular maintenance projects also include addition –alteration, modification activities. Increase in the student admission, new departments, and sections, new laboratories and workshops, up gradation of existing laboratories are very common in the institutional buildings in the developing nations like India. The project becomes very critical because it undergoes space problem, architectural design issues, structural modification, etc. One of the prime factors in the institutional building maintenance and modification project is the time constraint. Mostly it required being executed a specific non-work time period. The present research considered only the institutional buildings of the Eastern part of India to analyse the repair and maintenance project delay. A general survey was conducted among the technical institutes to find the causes and corresponding nature of construction delay factors. Five technical institutes are considered in the present study with repair, renovation, modification and extension type of projects. Construction delay factors are categorically subdivided into four groups namely, material, manpower (works), Contract and Site. The survey data are collected for the nature of delay responsible for a specific project and the absolute amount of delay through proposed and actual duration of work. In the first stage of the paper, a relative importance index (RII) is proposed for the delay factors. The occurrence of the delay factors is also judged by its frequency-severity nature. Finally, the delay factors are then rated and linked with the type of work. In the second stage, a regression analysis is executed to establish an empirical relationship between the actual time of a project and the percentage of delay. It also indicates the impact of the factors for delay responsibility. Ultimately, the present paper makes an effort to identify the critical delay factors for the repair and renovation type project in the Eastern Indian Institutional building.

Keywords: delay factor, institutional building, maintenance, relative importance index, regression analysis, repair

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Authors: Murat Erkoc, Kadir Ertogral

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Maintenance, repair and overhauling (MRO) of high cost equipment used in many industries such as transportation, military and construction are typically subject to regulations set by local governments or international agencies. Aircrafts are prime examples for this kind of equipment. Such equipment must be overhauled at certain intervals for continuing permission of use. As such, the overhaul must be completed by strict deadlines, which often times cannot be exceeded. Due to the fact that the overhaul is typically a long process, MRO companies carry so called rotable inventory for exchange of expensive modules in the overhaul process of the equipment so that the equipment continue its services with minimal interruption. The extracted module is overhauled and returned back to the inventory for future exchange, hence the name rotable inventory. However, since the rotable inventory and overhaul capacity are limited, it may be necessary to carry out some of the exchanges earlier than their deadlines in order to produce a feasible overhaul schedule. An early exchange results with a decrease in the equipment’s cycle time in between overhauls and as such, is not desired by the equipment operators. This study introduces an integer programming model for the optimal overhaul and exchange scheduling. We assume that there is certain number of rotables at hand at the beginning of the planning horizon for a single type module and there are multiple demands with known deadlines for the exchange of the modules. We consider an MRO system with identical parallel processing lines. The model minimizes total earliness by generating optimal overhaul start times for rotables on parallel processing lines and exchange timetables for orders. We develop a fast exact solution algorithm for the model. The algorithm employs full-delay scheduling approach with backward allocation and can easily be used for overhaul scheduling problems in various MRO settings with modular rotable items. The proposed procedure is demonstrated by a case study from the aerospace industry.

Keywords: rotable inventory, full-delay scheduling, maintenance, overhaul, total earliness

Procedia PDF Downloads 518

Authors: Deenadayalan Govender

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In South Africa, similar to nations globally, the prevailing tangible link between people and the state is public infrastructure. Services delivered through infrastructure to the people and to the state form a critical enabler for social development in communities and economic development in the country. In this regard, infrastructure, being the backbone to a nation’s prosperity, ideally should be effectively maintained for seamless delivery of services. South African infrastructure is in a state of deterioration, which is leading to infrastructure dysfunction and collapse and is negatively affecting development of the economy. This infrastructure deterioration stems from deficiencies in maintenance practices and strategies. From the birth of South African democracy, government has pursued socio-economic transformation and the delivery of critical basic services to decrease the broadening boundaries of disparity. In this regard, the National Infrastructure Plan borne from strategies encompassed in the National Development Plan is given priority by government in delivering strategic catalytic infrastructure projects. The National Infrastructure Plan is perceived to be the key in unlocking opportunities that generate economic growth, kerb joblessness, alleviate poverty, create new entrepreneurial prospects, and mitigate population expansion and rapid urbanisation. Socio-economic transformation benefits from new infrastructure spend is not being realised as initially anticipated. In this context, South Africa is currently in a state of weakening economic growth, with further amassed levels of joblessness, unremitting poverty and inequality. Due to investor reluctance, solicitation of strategic infrastructure funding is progressively becoming a debilitating challenge in all government institutions. Exacerbating these circumstances further, is substandard functionality of existing infrastructure subsequent to inadequate maintenance practices. This in-depth multi-sectoral study into the state of infrastructure is to understand the principal reasons for infrastructure functionality regression better; furthermore, prioritised investigations into progressive maintenance strategies is focused upon. Resultant recommendations reveal enhanced maintenance strategies, with a vision to capitalize on infrastructure design life, and also give special emphasis to socio-economic development imperatives in the long-term. The research method is principally based on descriptive methods (survey, historical, content analysis, qualitative).

Keywords: infrastructure, maintenance, socio-economic, strategies

Procedia PDF Downloads 117

Authors: Janaina Pereira, Barbara Gonzalez, Valentina Chitas, Teresa Molina

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Intro: The positive impact of opioid maintenance programs on the health of heroin addicts, and on public health in general, has been widely recognized, namely on the prevalence reduction of infectious diseases as HIV, and on the social reintegration of this population. Nevertheless, a part of patients in these programs cannot remain heroin abstinent, or has relapses, during the treatment. Method: Thus, this cross-sectional research aims at analyzing the relation between a set of psychological and psychosocial variables, which have been associated with the onset of heroin use, and assess if they are also associated with absence of abstinence in participants in an opioid maintenance program. A total of 62 patients, aged between 26 and 58 years old (M= 40.87, DP= 7.39) with a time in opioid maintenance program between 1 and 10 years (M= 5.42, DP= 3.05), 77.4% male and 22.6% female, participated in this research. To assess the criterion variable (heroin use) we used the mean value of positive results in urine tests during the participation in the program, weighted according to the number of months in program. The predictor variables were the coping strategies, the dispositional sensation seeking, and the existence of Posttraumatic stress disorder (PTSD). Results: The results showed that only 33.87% of the patients were totally abstinent of heroin use since the beginning of the program, and the absence of abstinence, as the number of positive heroin tests, was primarily predicted by less proactive coping, and secondarily by a higher level of sensation seeking. 16.13% of the sample fulfilled diagnosis criteria for PTSD, and 67.74 % had at least one traumatic experience throughout their lives. The total of PTSD symptoms had a positive correlation with the number of physical health problems, and with the lack of professional occupation. These results have several implications for the clinical practice in this field, and we suggest the promotion of proactive coping strategies should integrate these opioid maintenance programs, as they represent the tendency to face future events as challenges and opportunities, being positively related to positive results on several fields. The early identification of PTSD in the participants, before entering the opioid maintenance programs, would be important as it is related to negative features that hinder social reintegration, Finally, to identify individuals with a sensation seeking profile would be relevant, not only because they face a higher risk of relapse, but also because the therapeutical approaches should not ignore this dispositional feature in the alternatives they propose to the patients.

Keywords: opioid maintenance programs, proactive coping, PTSD, sensation seeking

Procedia PDF Downloads 98

Authors: T. Bensana, S. Mekhilef

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The methodology of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. The ability of wavelet analysis to efficiently detect non-stationary, non-periodic, transient features of the vibration signal makes it a demanding tool for condition monitoring. This paper presents a methodology for fault diagnosis of rolling element bearings based on wavelet envelope power spectrum technique is analysed in both the time and frequency domains. In the time domain the auto-correlation of the wavelet de-noised signal is applied to evaluate the period of the fault pulses. However, in the frequency domain the wavelet envelope power spectrum has been used to identify the fault frequencies with the single sided complex Laplace wavelet as the mother wavelet function. Results show the superiority of the proposed method and its effectiveness in extracting fault features from the raw vibration signal.

Keywords: preventive maintenance, fault diagnostics, rolling element bearings, wavelet de-noising

Procedia PDF Downloads 347

Authors: T. Prabakaran

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Gas Insulated Substations (GIS) are widely used as important switchgear equipment because of its high reliability, low space requirement, low risk factor and easy maintenance, yet some failures have been reported. Some of the failures are due to presence of metallic particles inside the GIS compartment. The defect can be generated in GIS during production, maintenance, installation and can be due to ageing of the component. The Ultra-High Frequency (UHF) method is used to diagnose the insulation condition of GIS by detecting the PD signals in GIS. This paper identifies PD patterns for free moving particle defect and particle fixed on cone using UHF method. As insulation failure usually starts with PD activity, this paper investigates the differences in PD characteristics in SF6 gas with different types of defects. Experimental results show that correct identification of defects can be achieved based on considered PD characteristics. The method can be applied to prove the quality of assembly work at commissioning, also on a regular basis after many years in service to detect aged and conducting particles as a part of the condition based maintenance.

Keywords: gas insulated substation, partial discharge, free moving particle defect, particle fixed on cone defect, ultra high frequency method

Procedia PDF Downloads 210

Authors: Luciano Oliveira, Elsa Vásquez-Alvarez

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The increase in rail freight transport in Brazil in recent years requires new railway lines and the maintenance of existing ones, which generates high costs for concessionaires. It is in this context that this work is inserted, whose objective is to propose a method that uses Binary Linear Programming for the choice of sleeper and rail fastening, from various options, including the way to apply these materials, with focus to minimize costs. Unit value information, the life cycle each of material type, and service expenses are considered. The model was implemented in commercial software using real data for its validation. The formulated model can be replicated to support decision-making for other railway projects in the choice of sleepers and rail fastening with lowest cost.

Keywords: linear programming, rail fastening, rail sleeper, railway

Procedia PDF Downloads 177

Authors: Bedilu Tafesse

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Conservation of crop genetic resources presents a challenge of identifying specific determinants driving maintenance of diversity at farm and agroecosystems. The objectives of this study were to identify socioeconomic, market and agroecological determinants of farmers’ maintenance of wheat diversity at the household level and derive implications for policies in designing on-farm conservation programs. We assess wheat diversity at farm level using household survey data. A household decision making model is conceptualized using microeconomic theory to assess and identify factors influencing on-farm rice diversity. The model is then tested econometrically by using various factors affecting farmers’ variety choice and diversity decisions. The findings show that household-specific socioeconomic, agroecological and market factors are important in determining on-farm wheat diversity. The significant variables in explaining richness and evenness of wheat diversity include distance to the nearest market, subsistence ratio, modern variety sold, land types and adult labour working in agriculture. The statistical signs of the factors determining wheat diversity are consistent in explaining the richness, dominance and evenness among rice varieties. Finally, the study implies that the cost-effective means of promoting and sustaining on-farm conservation programmes is to target them in market isolated geographic locations of high crop diversity where farm households have more heterogeneity of agroecological conditions and more active family adult labour working on-farm.

Keywords: diversity indices, dominance, evenness, on-farm conservation, wheat diversity, richness

Procedia PDF Downloads 278

Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

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During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

Procedia PDF Downloads 50

Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

Procedia PDF Downloads 163

Authors: Vural Erdogan

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We created two-dimensional hexagonal cellular automata to obtain complexity by using simple rules same as Conway’s game of life. Considering the game of life rules, Wolfram's works about life-like structures and John von Neumann's self-replication, self-maintenance, self-reproduction problems, we developed 2-states and 3-states hexagonal growing algorithms that reach large populations through random initial states. Unlike the game of life, we used six neighbourhoods cellular automata instead of eight or four neighbourhoods. First simulations explained that whether we are able to obtain sort of oscillators, blinkers, and gliders. Inspired by Wolfram's 1D cellular automata complexity and life-like structures, we simulated 2D synchronous, discrete, deterministic cellular automata to reach life-like forms with 2-states cells. The life-like formations and the oscillators have been explained how they contribute to initiating self-maintenance together with self-reproduction and self-replication. After comparing simulation results, we decided to develop the algorithm for another step. Appending a new state to the same algorithm, which we used for reaching life-like structures, led us to experiment new branching and fractal forms. All these studies tried to demonstrate that complex life forms might come from uncomplicated rules.

Keywords: hexagonal cellular automata, self-replication, self-reproduction, self- maintenance

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Authors: Kiyoumars Roushangar, Saeid Sadaghian

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Although accurate sediment load prediction is very important in planning, designing, operating and maintenance of water resources structures, the transport mechanism is complex, and the deterministic transport models are based on simplifying assumptions often lead to large prediction errors. In this research, firstly, two intelligent ANN methods, Radial Basis and General Regression Neural Networks, are adopted to model of total sediment load transport into Madani Dam reservoir (north of Iran) using the measured data and then applicability of the sediment transport methods developed by Engelund and Hansen, Ackers and White, Yang, and Toffaleti for predicting of sediment load discharge are evaluated. Based on comparison of the results, it is found that the GRNN model gives better estimates than the sediment rating curve and mentioned classic methods.

Keywords: sediment transport, dam reservoir, RBF, GRNN, prediction

Procedia PDF Downloads 472

Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner

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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.

Keywords: radio base station, maintenance, classification, detection, deep learning, automation

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Authors: Ionel Botef

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Studies show that, for viable product realisation and maintenance, a spectrum of novel processing technologies and materials to improve performance and reduce costs and environmental impact must constantly be addressed. One of these technologies, namely the cold spray process, has enabled a broad range of coatings and applications, including many that have not been previously possible or commercially practical, hence its potential for new aerospace, electronics, or medical applications. Therefore, the purpose of this paper is to summarise the state of the art of this technology alongside its theoretical and experimental studies, and explore the role and impact of cold spraying on surface engineering.

Keywords: surface engineering, cold spray, ageing aircrafts, corrosion, microchannels, maintenance

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Authors: Mojahid F. Saeed Osman

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Repairable items inventory system is a management tool used to incorporate all information concerning inventory levels and movements for repaired and new items. This paper presents development of an effective simulation model for managing the inventory of repairable items for a production system where production lines send their faulty items to a repair shop considering the stochastic failure behavior and repair times. The developed model imitates the process of handling the on-hand inventory of repaired items and the replenishment of the inventory of new items using Economic Order Quantity and Reorder Point ordering policy in a flexible and risk-free environment. We demonstrate the appropriateness and effectiveness of the proposed simulation model using an illustrative case problem. The developed simulation model can be used as a reliable tool for estimating a healthy on-hand inventory of new and repaired items, backordered items, and downtime due to unavailability of repaired items, and validating and examining Economic Order Quantity and Reorder Point ordering policy, which would further be compared with other ordering strategies as future work.

Keywords: inventory system, repairable items, simulation, maintenance, economic order quantity, reorder point

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Authors: Avan Al-Saffar, Eun-Jin Kim

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Both the logistic growth model and the gompertz growth model are used to describe growth processes. Both models driven by perturbations in different cases are investigated using information theory as a useful measure of sustainability and the variability. Specifically, we study the effect of different oscillatory modulations in the system's parameters on the evolution of the system and Probability Density Function (PDF). We show the maintenance of the initial conditions for a long time. We offer Fisher information analysis in positive and/or negative feedback and explain its implications for the sustainability of population dynamics. We also display a finite amplitude solution due to the purely fluctuating growth rate whereas the periodic fluctuations in negative feedback can lead to break down the system's self-regulation with an exponentially growing solution. In the cases tested, the gompertz and logistic systems show similar behaviour in terms of information and sustainability although they develop differently in time.

Keywords: dynamical systems, fisher information, probability density function (pdf), sustainability

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Authors: Manita Ale, Ganesh P. Shivakoti, Ram C. Bastakoti

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Irrigation system, a system for enhancing agricultural productivity, requires regular maintenance in order to avoid irregular allocation of water. For maintenance of the system in long run, farmers’ participation plays a key role increasing the performance of system. The performance of any irrigation system mainly relies on various factors which affect collective action plus decision making, as well as their shared impacts. The paper consists of system level information that were collected from 12 Irrigation Systems (IS) from three-sampled districts of Nepal and the household information that were collected from 160 irrigation water users. The results reveal that, out of 12 sampled irrigation systems, only 4 systems shows high performance levels. The high performance level of those systems was characterized on the basis of adequate availability of water, good maintenance of system infrastructure, and conformance to existing rules followed. In addition, the paper compares different irrigation systems based on trust, reciprocity, cropping intensity, command area and yield as tools to indicate the importance of collective action in performance of irrigation system.

Keywords: collective action, decision-making, farmers’ participation, performance

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Authors: Victoria Bessonova, Robert Dorrell, Nina Dethlefs, Evdokia Tapoglou, Katharine York

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As the global pursuit of renewable energy intensifies, offshore wind farms have emerged as a promising solution to combat climate change. The global offshore wind installed capacity is projected to increase 56-fold by 2055. However, the impacts of climate change, particularly changes in wave climate, are not widely understood. Offshore wind installation and maintenance activities often require specific weather windows, characterized by calm seas and low wave heights, to ensure safe and efficient operations. However, climate change-induced alterations in wave characteristics can reduce the availability of suitable weather windows, leading to delays and disruptions in project timelines. it applied the operational limits of installation and maintenance vessels to past and future climate wave projections. This revealed changes in the annual and monthly accessibility of offshore wind farms at key global development locations. When accessibility is only defined by significant wave height, spatial patterns in the annual accessibility roughly follow changes in significant wave height, with increased availability where significant wave height is decreasing. This resulted in a 1-6% increase in Europe and North America and a similar decrease in South America, Australia and Asia. Monthly changes suggest unchanged or slightly decreased (1-2%) accessibility in summer months and increased (2-6%) in winter. Further assessment includes assessing the sensitivity of accessibility to operational limits defined by wave height combined with wave period and wave height combined with wind speed. Results of this assessment will be included in the presentation. These findings will help stakeholders inform climate change adaptations in installation and maintenance planning practices.

Keywords: climate change, offshore wind, offshore wind installation, operations and maintenance, wave climate, wind farm accessibility

Procedia PDF Downloads 55

Authors: Chanya Inprasit, Ching-Liang Hsieh, Yi-Wen Lin

Abstract:

Obesity (OB) is a hazardous global health problem that has been increasing in prevalence, more severely in last decade. It is the mainly resultant from the imbalance between food consumption and energy expenditure, which is concordant with a modern lifestyle, implying an increase in calories with poorer quality of food intake accompanied by a decrease in physical activities. Obesity does not concern the appearance only but is also a major factor contributing to poor physiology, psychology, society and economic issues. Moreover, OB induces low-grade inflammation in the body through the regulatory effect it enacts on the adipocyte function. Various alternative treatments were investigated for body weight control, including Acupoint Catgut Embedding (ACE). ACE is the implantation of absorbable catgut sutures at specific acupoints, displaying durable and potent stimulation and thereby reducing the treatment frequency. Our study utilized a mouse model to exclude any psychological factors of OB and ACE treatment. High-fat diet and body weight were measured once a week before subjects in ACE and Sham group received the ACE treatment or placebo treatment. We hypothesized that ACE can control body weight through the interaction of the TRPV1 pathways, as TRPV1 accordingly responds to inflammatory factors. The results of body weight variation show a significant decrease in body weight in ACE group compared with the baseline of control and Sham group. Meanwhile, converse results were explored in TRPV1 knockout mice, where a significant maintenance of normal body weight throughout the experiment period was observed. There was no significant difference in food consumption of each group. These finding indicated that TRPV1 pathways and its associated pathways may be involved in the maintenance of body weight, which can be controlled by ACE treatment of genetic manipulation.

Keywords: acupoint catgut embedding, obesity, hypothalamus, TRPV1

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Authors: Jan-Peter Mund, Christian Kind

Abstract:

In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

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Authors: Mojtaba Valinejadshoubi, Azin Shakibabarough, Ashutosh Bagchi

Abstract:

Facilities such as residential buildings, office buildings, and hospitals house large density of occupants. Therefore, a low-cost facility management program (FMP) should be used to provide a satisfactory built environment for these occupants. Facility management (FM) has been recently used in building projects as a critical task. It has been effective in reducing operation and maintenance cost of these facilities. Issues of information integration and visualization capabilities are critical for reducing the complexity and cost of FM. Building information modeling (BIM) can be used as a strong visual modeling tool and database in FM. The main objective of this study is to examine the applicability of BIM in the FM process during a building’s operational phase. For this purpose, a seven-storey office building is modeled Autodesk Revit software. Authors integrated the cloud-based environment using a visual programming tool, Dynamo, for the purpose of having a real-time cloud-based communication between the facility managers and the participants involved in the project. An appropriate and effective integrated data source and visual model such as BIM can reduce a building’s operational and maintenance costs by managing the building life cycle properly.

Keywords: building information modeling, facility management, operational phase, building life cycle

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Authors: K. Jahani, J. Razavi

Abstract:

Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.

Keywords: computer aided engineering, predictive maintenance, fault detection, mining process line, slurry pump, hydrocyclone

Procedia PDF Downloads 383

Authors: Entisar A. Elgmati

Abstract:

Recurrent infant diarrhea is studied using daily data collected in Salvador, Brazil over one year and three months. A logistic regression model is fitted instead of Aalen's additive model using the same covariates that were used in the analysis with the additive model. The model gives reasonably similar results to that using additive regression model. In addition, the problem with the estimated conditional probabilities not being constrained between zero and one in additive model is solved here. Also martingale residuals that have been used to judge the goodness of fit for the additive model are shown to be useful for judging the goodness of fit of the logistic model.

Keywords: additive model, cumulative probabilities, infant diarrhoea, recurrent event

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Authors: Ahmed Guerine, Ali El Hafidi, Bruno Martin, Philippe Leclaire

Abstract:

This paper investigates the nonlinear dynamic response of a mechanical torque limiter which is used to protect drive parts from overload (helical transmission gears). The system is driven by four excitations: two external excitations (aerodynamics torque and force) and two internal excitations (two mesh stiffness fluctuations). In this work, we develop a dynamic model with lumped components and 28 degrees of freedom. We use the Runge Kutta step-by-step time integration numerical algorithm to solve the equations of motion obtained by Lagrange formalism. The numerical results have allowed us to identify the sources of vibration in the wind turbine. Also, they are useful to help the designer to make the right design and correctly choose the times for maintenance.

Keywords: two-stage helical gear, lumped model, dynamic response, torque-limiter

Procedia PDF Downloads 329