Search results for: breakdown maintenance

Authors: Awadh O. Al-Anazi

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As part of the organizational needs toward successful maintaining activities, a proper management system need to be put in place, ensuring the effectiveness of maintenance activities. The management system shall clearly describes the process of identifying, prioritizing, planning, scheduling, execution, and providing valuable feedback for all maintenance activities. Completion and accuracy of the system with proper implementation shall provide the organization with a strong platform for effective maintenance activities that are resulted in efficient outcomes toward business success. The purpose of this research was to introduce a practical tool for measuring the maintenance efficiency level within Saudi organizations. A comprehensive study was launched across many maintenance professionals throughout Saudi leading organizations. The study covered five main categories: work process, identification, planning and scheduling, execution, and performance monitoring. Each category was evaluated across many dimensions to determine its current effectiveness through a five-level scale from 'process is not there' to 'mature implementation'. Wide participation was received, responses were analyzed, and the study was concluded by highlighting major gaps and improvement opportunities within Saudi organizations. One effective implementation of the efficiency enhancement efforts was deployed in Saudi Kayan (one of Sabic affiliates). Below details describes the project outcomes: SK overall maintenance wrench time was measured at 20% (on average) from the total daily working time. The assessment indicates the appearance of several organizational gaps, such as a high amount of reactive work, poor coordination and teamwork, Unclear roles and responsibilities, as well as underutilization of resources. Multidiscipline team was assigned to design and implement an appropriate work process that is capable to govern the execution process, improve the maintenance workforce efficiency, and maximize wrench time (targeting > 50%). The enhanced work process was introduced through brainstorming and wide benchmarking, incorporated with a proper change management plan and leadership sponsorship. The project was completed in 2018. Achieved Results: SK WT was improved to 50%, which resulted in 1) reducing the Average Notification completion time. 2) reducing maintenance expenses on OT and manpower support (3.6 MSAR Actual Saving from Budget within 6 months).

Keywords: efficiency, enhancement, maintenance, work force, wrench time

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Authors: Dipankar Deka

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Exploration and production of Oil & Gas is a very challenging business on which a nation’s energy security depends on. The exploration and Production of hydrocarbon is a very precise and time-bound process. The striking rate of hydrocarbon in a drilled well is so uncertain that the success rate is only 31% in 2021 as per Rigzone. Huge cost is involved in drilling as well as the production of hydrocarbon from a well. Due to this very reason, no one can effort to lose a well because of faulty machines, which increases the non-productive time (NPT). Numerous activities that include manpower and machines synchronized together works in a precise way to complete the full cycle of exploration, rig movement, drilling and production of crude oil. There are several machines, both fixed and mobile, are used in the complete cycle. Most of these machines have a tight schedule of work operating in various drilling sites that are simultaneously being drilled, providing a very narrow window for maintenance. The shutdown of any of these machines for even a small period of time delays the whole project and increases the cost of production of hydrocarbon by manifolds. Moreover, these machines are custom designed exclusively for oil field operations to be only used in Mining Exploration Licensed area (MEL) earmarked by the government and are imported and very costly in nature. The cost of some of these mobile units like Well Logging Units, Coil Tubing units, Nitrogen pumping units etc. that are used for Well stimulation and activation process exceeds more than 1 million USD per unit. So the increase of self-life of these units also generates huge revenues during the extended duration of their services. In this paper we are considering the very critical mobile oil field equipment like Well Logging Unit, Coil Tubing unit, well-killing unit, Nitrogen pumping unit, MOL Oil Field Truck, Hot Oil Circulation Unit etc., and their extensive preventive maintenance in our auto workshop. This paper is the outcome of 10 years of structured automobile maintenance and minute documentation of each associated event that allowed us to perform the comparative study between the new practices of preventive maintenance over the age-old practice of system-based corrective maintenance and its impact on the self-life of the equipment.

Keywords: automobile maintenance, preventive maintenance, symptom based maintenance, workshop technologies

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Authors: Mina Sadat Orooje, Mohammad Mehdi Latifi, Behnam Fereydooni Eftekhari

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The integration of cutting-edge Internet of Things (IoT) technologies with advanced Artificial Intelligence (AI) systems is revolutionizing healthcare facility management. However, the current landscape of hospital building maintenance suffers from slow, repetitive, and disjointed processes, leading to significant financial, resource, and time losses. Additionally, the potential of Building Information Modeling (BIM) in facility maintenance is hindered by a lack of data within digital models of built environments, necessitating a more streamlined data collection process. This paper presents a robust framework that harmonizes AI with BIM-IoT technology to elevate healthcare Facility Maintenance Management (FMM) and address these pressing challenges. The methodology begins with a thorough literature review and requirements analysis, providing insights into existing technological landscapes and associated obstacles. Extensive data collection and analysis efforts follow to deepen understanding of hospital infrastructure and maintenance records. Critical AI algorithms are identified to address predictive maintenance, anomaly detection, and optimization needs alongside integration strategies for BIM and IoT technologies, enabling real-time data collection and analysis. The framework outlines protocols for data processing, analysis, and decision-making. A prototype implementation is executed to showcase the framework's functionality, followed by a rigorous validation process to evaluate its efficacy and gather user feedback. Refinement and optimization steps are then undertaken based on evaluation outcomes. Emphasis is placed on the scalability of the framework in real-world scenarios and its potential applications across diverse healthcare facility contexts. Finally, the findings are meticulously documented and shared within the healthcare and facility management communities. This framework aims to significantly boost maintenance efficiency, cut costs, provide decision support, enable real-time monitoring, offer data-driven insights, and ultimately enhance patient safety and satisfaction. By tackling current challenges in healthcare facility maintenance management it paves the way for the adoption of smarter and more efficient maintenance practices in healthcare facilities.

Keywords: artificial intelligence, building information modeling, healthcare facility maintenance, internet of things integration, maintenance efficiency

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Authors: Mahmoud Saleh Mohammed Alkathy

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Electricity generated from renewable resources may help the transition to clean energy. A reliable energy storage system is required to use this energy properly. To do this, a high breakdown strength (Eb) and a significant difference between spontaneous polarization (Pmax) and remnant polarization (Pr) are required. To achieve this, the defect dipoles in lead free BaTiO3 ferroelectric ceramics are created using Mg2+ and Ni2+ ions as acceptor co-doping in the Ti site. According to the structural analyses, the co-dopant ions were effectively incorporated into the BTO unit cell. According to the ferroelectric study, the co-doped samples display a double hysteresis loop, stronger polarization, and high breakdown strength. The formation of oxygen vacancies and defect dipoles prevent domains' movement, resulting in hysteresis loop pinching. This results in increased energy storage density and efficiency. The defect dipoles mechanism effect can be considered a fascinating technology that can guide the researcher working on developing energy storage for next-generation applications.

Keywords: microstructure, defect, energy storage, effciency

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Authors: Manjinder Singh, Anish Sachdeva

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Research on total quality management (TQM), total productive maintenance (TPM), international organization for standardization (ISO) and six sigma generally investigate the implementation and impact of these programs in isolation. However, none of these quality improvement programs is self-sufficient and they may not be powerful enough to deliver the improvements and innovations that are required nowadays to ensure the survival and growth of a firm. They are not mutually exclusive and inconsistent. On the contrary, they need complementary support and may reinforce mutually to make use of their complementarity, inducement of side-effects in favor of other quality improvement program, mutual simulation and exploitation of shared values. In this paper, first of all, the various management activities were identified which are normally under focus when any quality improvement program is implemented in any organization. Then TOPSIS methodology was applied to establish the ranking of various quality improvement programs (total quality management, total productive maintenance, ISO and six sigma which were brought to the corporate boardroom to improve the quality) with respect to different management activities (operations related activities, quality related activities, maintenance related activities, organizational related activities, human related activities and finance related activities).

Keywords: total productive maintenance (TPM), total quality management (TQM), TOPSIS, international organization for standardization (ISO)

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Authors: Pedro Miguel Cardoso Carneiro, Ricardo André Nunes Borges, João Pedro Soares Loureiro, Hermínio Maio Graça Fernandes

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Wind energy has been exponentially growing over the last years and will allow countries to progress regarding the decarbonization objective. In parallel, the maintenance activities have also been increasing in consequence of ageing and deterioration of the wind farms. The time available for wind blade maintenance is given by the weather window that is based upon weather conditions. Most of the wind blade repair and maintenance activities require a narrow window of temperature and humidity. Due to this limitation, the current weather windows result only on approximately 35% days/year are used for maintenance, that takes place mostly during summertime. This limitation creates large economic losses in the energy production of the wind towers, since they can be inoperative or with the energy production output reduced for days or weeks due to existing damages. Another important aspect is that the maintenance costs are higher due to the high standby time and seasonality imposed on the technicians. To reduce the relevant maintenance costs of blades and energy loses some technological developments were carried out to significantly improve this reality. The focus of this activity was to develop a series of key developments to have in the near future a suspended access equipment that can operate in harsh conditions, wind rain, cold/hot environment. To this end we have identified key areas that need to be revised and require new solutions to be found; a habitat system, multi-configurable roof and floor, roof and floor interface to blade, secondary attachment solutions to the blade and to the tower. On this paper we will describe the advances produced during a national R&D project made in partnership with an end-user (Onrope) and a test center (ISQ).

Keywords: wind turbine maintenance, cost reduction, technological innovations, wind turbine blade

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Authors: Muhammad Bilal, Adil Ahmed

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This study proposes an approach for electrical induction motor predictive maintenance utilizing machine learning algorithms. On the basis of a study of temperature data obtained from sensors put on the motor, the goal is to predict motor failures. The proposed models are trained to identify whether a motor is defective or not by utilizing machine learning algorithms like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). According to a thorough study of the literature, earlier research has used motor current signature analysis (MCSA) and vibration data to forecast motor failures. The temperature signal methodology, which has clear advantages over the conventional MCSA and vibration analysis methods in terms of cost-effectiveness, is the main subject of this research. The acquired results emphasize the applicability and effectiveness of the temperature-based predictive maintenance strategy by demonstrating the successful categorization of defective motors using the suggested machine learning models.

Keywords: predictive maintenance, electrical induction motors, machine learning, temperature signal methodology, motor failures

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Authors: Jongho Park, Ui-Cheol Shin, Jinwoong Choi, Sungnam Hong, Sun-Kyu Park

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While the numerous structures built the industrialization are aging, the effort for the maintenance is concentrated in many countries. However, the traffic jam, environmental damage, and enormous maintenance cost, and etc become a problem. So, in order to solve this, the modular bridge has been studied. This bridge is the structure which utilizes and assembles the standard precast member. Through this, the substitution of the existing bridge and advantage of the easy maintenance will be achieved. However, the reliability in the long-term behavior is insufficient due to the junction part between modular precast members. Therefore, in this research, the cyclic load loading experiment was performed on the junction and deflection was analyzed by long-term service in modular slab connection. The deflection of modular slab with junction was mostly generated when initial and final test.

Keywords: modular bridge, deflection, cyclic loading, junction

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Authors: Lazlo Fauth, Andreas Ligocki

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For cost-effective use of harvesters, expensive repairs and unplanned downtimes must be reduced as far as possible. The predictive detection of failing systems and the calculation of intelligent service intervals, necessary to avoid these factors, require in-depth knowledge of the machines' behavior. Such know-how needs permanent monitoring of the machine state from different technical perspectives. In this paper, three approaches will be presented as they are currently pursued in the publicly funded project PreForst at Ostfalia University of Applied Sciences. These include the intelligent linking of workshop and service data, sensors on the harvester, and a special online hydraulic oil condition monitoring system. Furthermore the paper shows potentials as well as challenges for the use of these data in the conception of a predictive maintenance system.

Keywords: predictive maintenance, condition monitoring, forest harvesting, forest engineering, oil data, hydraulic data

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Authors: Nilesh Pancholi, Mangal Bhatt

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This paper highlights comparative results of traditional FMECA and multi-factor decision-making approach based on “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)” for aluminum wire rolling mill plant. The suggested study is carried out to overcome the limitations of FMECA by assigning the scores against each failure modes in crisp values to evaluate the criticalities of the failure modes without uncertainty. The primary findings of the paper are that sudden impact on the rolls seems to be most critical failure cause and high contact stresses due to rolling & sliding action of mesh to be least critical failure cause. It is suggested to modify the current control practices with proper maintenance strategy based on achieved maintainability criticality index (MCI). The outcome of the study will be helpful in deriving optimized maintenance plan to maximize the performance of continuous process industry.

Keywords: reliability, maintenance, FMECA, TOPSIS, process industry

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Authors: Mohamed Bin Alhaj, Hexu Liu, Mohammed Sulaiman, Osama Abudayyeh

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An effective facility maintenance management (FMM) system plays a crucial role in improving the quality of services and maintaining the facility in good condition. Current FMM heavily relies on the quality of the data collection function of the FMM systems, at times resulting in inefficient FMM decision-making. The new technology-based crowdsourcing provides great potential to improve the current FMM practices, especially in terms of timeliness and quality of data. This research aims to investigate the feasibility of using new technology-driven crowdsourcing for FMM and highlight its opportunities and challenges. A survey was carried out to understand the human, data, system, geospatial, and automation characteristics of crowdsourcing for an educational campus FMM via social networks. The survey results were analyzed to reveal the challenges and recommendations for the implementation of crowdsourcing for FMM. This research contributes to the body of knowledge by synthesizing the challenges and opportunities of using crowdsourcing for facility maintenance and providing a road map for applying crowdsourcing technology in FMM. In future work, a conceptual framework will be proposed to support data-driven FMM using social networks.

Keywords: crowdsourcing, facility maintenance management, social networks

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Authors: S. Werrlich, E. Eichstetter, K. Nitsche, G. Notni

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Augmented Reality (AR) is a strong growing research topic in different training domains such as medicine, sports, military, education and industrial use cases like assembly and maintenance tasks. AR claims to improve the efficiency and skill-transfer of training tasks. This paper gives a comprehensive overview of evaluations using AR for assembly and maintenance training tasks published between 1992 and 2017. We search in a structured way in four different online databases and get 862 results. We select 17 relevant articles focusing on evaluating AR-based training applications for assembly and maintenance tasks. This paper also indicates design guidelines which are necessary for creating a successful application for an AR-based training. We also present five scientific limitations in the field of AR-based training for assembly tasks. Finally, we show our approach to solve current research problems using Design Science Research (DSR).

Keywords: assembly, augmented reality, survey, training

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Authors: Amauri Josafat Gomez Aguilar, Jean Pierre Kenné

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This research presents the development of a self-sustainable manufacturing system from a circular economy perspective, structured by a multi-stage serial production system consisting of a series of machines under deterioration in charge of producing a single product and a reverse remanufacturing system constituted by the same productive systems of the first scheme and different tooling, fed by-products collected at the end of their life cycle, and non-conforming elements of the first productive scheme. Since the advanced production manufacturing system is unable to satisfy the customer's quality expectations completely, we propose the development of a mixed integer linear mathematical model focused on the optimal search and assignment of quality stations and preventive maintenance operation to the machines over a time horizon, intending to segregate the correct number of non-conforming parts for reuse in the remanufacturing system and thereby minimizing production, quality, maintenance, and customer non-conformance penalties. Numerical experiments are performed to analyze the solutions found by the model under different scenarios. The results showed that the correct implementation of a closed manufacturing system and allocation of quality inspection and preventive maintenance operations generate better levels of customer satisfaction and an efficient manufacturing system.

Keywords: closed loop, mixed integer linear programming, preventive maintenance, quality inspection

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Authors: Leila Jafari, Viliam Makis

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In this paper, the joint optimization of the economic manufacturing quantity (EMQ), safety stock level, and condition-based maintenance (CBM) is presented for a partially observable, deteriorating system subject to random failure. The demand is stochastic and it is described by a Poisson process. The stochastic model is developed and the optimization problem is formulated in the semi-Markov decision process framework. A modification of the policy iteration algorithm is developed to find the optimal policy. A numerical example is presented to compare the optimal policy with the policy considering zero safety stock.

Keywords: condition-based maintenance, economic manufacturing quantity, safety stock, stochastic demand

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Authors: Rattanakorn Phadungthin, Juthathip Haema

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This research presents an assessment of the criticality of the substation's power transformer using the Entropy Weight method to enable more effective maintenance planning. Typically, transformers fail due to heat, electricity, chemical reactions, mechanical stress, and extreme climatic conditions. Effective monitoring of the insulating oil is critical to prevent transformer failure. However, finding appropriate weights for dissolved gases is a major difficulty due to the lack of a defined baseline and the requirement for subjective expert opinion. To decrease expert prejudice and subjectivity, the Entropy Weight method is used to optimise the weightings of eleven key dissolved gases. The algorithm to assess the criticality operates through five steps: create a decision matrix, normalise the decision matrix, compute the entropy, calculate the weight, and calculate the criticality score. This study not only optimises gas weighing but also greatly minimises the need for expert judgment in transformer maintenance. It is expected to improve the efficiency and reliability of power transformers so failures and related economic costs are minimized. Furthermore, maintenance schemes and ranking are accomplished appropriately when the assessment of criticality is reached.

Keywords: criticality assessment, dissolved gas, maintenance scheme, power transformer

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Authors: Abdelhadi Adel, Kadri Ouahab

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This paper proposes a hybrid algorithm for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

Keywords: multi-agent systems, emergence, genetic algorithm, makespan, systematic maintenance, scheduling, hybrid flow shop scheduling

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

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Authors: Adrian Craig, Jonathan Easaw, Rose Jordan, Ben Hall

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The prescription of intravenous fluids is a fundamental component of inpatient management, but it is one which usually lacks thought. Fluids are a drug, which like any other can cause harm when prescribed inappropriately or wrongly. However, it is well recognised that it is poorly done, especially in the acute portals. The National Institute for Health and Care Excellence (NICE) recommends 1mmol/kg of potassium, sodium, and chloride per day. With various options of fluids, clinicians tend to face difficulty in choosing the most appropriate maintenance fluid, and there is a reluctance to prescribe potassium as part of an intravenous maintenance fluid regime. The aim was to prospectively audit the prescription of the first bag of intravenous maintenance fluids, the use of urea and electrolytes results to guide the choice of fluid and the use of fluid prescription charts, in a busy emergency department of a major trauma centre in Stoke-on-Trent, United Kingdom. This was undertaken over a week in early November 2016. Of those prescribed maintenance fluid only 8.9% were prescribed a fluid which was most appropriate for their daily electrolyte requirements. This audit has helped to highlight further the issues that are faced in busy Emergency Departments within hospitals that are stretched and lack capacity for prompt transfer to a ward. It has supported the findings of NICE, that emergency admission portals such as Emergency Departments poorly prescribed intravenous fluid therapy. The findings have enabled simple steps to be taken to educate clinicians about their fluid of choice. This has included: posters to remind clinicians to consider the urea and electrolyte values before prescription, suggesting the inclusion of a suggested intravenous fluid of choice in the prescription chart of the trust and the inclusion of a session within the introduction programme revising intravenous fluid therapy and daily electrolyte requirements. Moving forward, once the interventions have been implemented then, the data will be reaudited in six months to note any improvement in maintenance fluid choice. Alongside this, an audit of the rate of intravenous maintenance fluid therapy would be proposed to further increase patient safety by avoiding unintentional fluid overload which may cause unnecessary harm to patients within the hospital. In conclusion, prescription of maintenance fluid therapy was poor within the Emergency Department, and there is a great deal of opportunity for improvement. Therefore, the measures listed above will be implemented and the data reaudited.

Keywords: chloride, electrolyte, emergency department, emergency medicine, fluid, fluid therapy, intravenous, maintenance, major trauma, potassium, sodium, trauma

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Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

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Authors: Wen Liang Chang

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In this study, the optimal replacement time and number of preventive maintenance (PM) actions were investigated for a second-hand device. Suppose that a user intends to use a second-hand device for manufacturing products, and that the device is replaced with a new one. Any device failure is rectified through minimal repair, thereby incurring a fixed repair cost to the user. If the new device fails within the FRW period, minimal repair is performed at no cost to the user. After the FRW expires, a failed device is repaired and the cost of repair is incurred by the user. In this study, two profit models were developed, and the optimal replacement time and number of PM actions were determined to maximize profits. Finally, the influence of the optimal replacement time and number of PM actions were elaborated on, using numerical examples.

Keywords: second-hand device, preventive maintenance, replacement time, device failure

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Authors: Ipek Kivanc, Demet Ozgur-Unluakin

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Over the past years, technological innovations and advancements have played an important role in the industrial world. Due to technological improvements, the degree of complexity of the systems has increased. Hence, all systems are getting more uncertain that emerges from increased complexity, resulting in more cost. It is challenging to cope with this situation. So, implementing efficient planning of maintenance activities in such systems are getting more essential. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for stochastic sequential decision problems under uncertainty. Although maintenance optimization in a dynamic environment can be modeled as such a sequential decision problem, POMDPs are not widely used for tackling maintenance problems. However, they can be well-suited frameworks for obtaining optimal maintenance policies. In the classical representation of the POMDP framework, the system is denoted by a single node which has multiple states. The main drawback of this classical approach is that the state space grows exponentially with the number of state variables. On the other side, factored representation of POMDPs enables to simplify the complexity of the states by taking advantage of the factored structure already available in the nature of the problem. The main idea of factored POMDPs is that they can be compactly modeled through dynamic Bayesian networks (DBNs), which are graphical representations for stochastic processes, by exploiting the structure of this representation. This study aims to demonstrate how maintenance planning of dynamic systems can be modeled with factored POMDPs. An empirical maintenance planning problem of a dynamic system consisting of four partially observable components deteriorating in time is designed. To solve the empirical model, we resort to Symbolic Perseus solver which is one of the state-of-the-art factored POMDP solvers enabling approximate solutions. We generate some more predefined policies based on corrective or proactive maintenance strategies. We execute the policies on the empirical problem for many replications and compare their performances under various scenarios. The results show that the computed policies from the POMDP model are superior to the others. Acknowledgment: This work is supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under grant no: 117M587.

Keywords: factored representation, maintenance, multi-component system, partially observable Markov decision processes

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Authors: L. V. S. S. Phaneendra Bolem, S. Shankar

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Low Volume Rural Roads (LVRRs) constitute an integral component of the road system in all countries. These encompass all aspects of the social and economic development of rural communities. It is known that on a worldwide basis the number of low traffic roads far exceeds the length of high volume roads. Across India, 90% of the roads are LVRRs, and they often form the most important link in terms of providing access to educational, medical, recreational and commercial activities in local and regional areas. In the recent past, Government of India (GoI), with the initiation of the ambitious programme namely 'Pradhan Mantri Gram Sadak Yojana' (PMGSY) gave greater importance to LVRRs realizing their role in economic development of rural communities. The vast expansion of the road network has brought connectivity to the rural areas of the country. Further, it is noticed that due to increasing axle loads and lack of timely maintenance, is accelerated the process of deterioration of LVRRs. In addition to this due to limited budget for maintenance of these roads systematic and scientific approach in utilizing the available resources has been necessitated. This would enable better prioritization and ranking for the maintenance and make ‘all-weather roads’. Taking this into account the present study has adopted a multi-criteria approach. The multi-criteria approach includes parameters such as social, economic, environmental and pavement condition as the main criterion and some sub-criteria to find the best suitable parameters and their weight. For this purpose the expert’s opinion survey was carried out using Delphi Technique (DT) considering Likert scale, pairwise comparison and ranking methods and entire data was analyzed. Finally, this study developed the maintenance criterion considering the socio-economic, environmental and pavement condition parameters for effective maintenance of low volume roads based on the engineering judgment.

Keywords: Delphi technique, experts opinion survey, low volume rural road maintenance, multi criteria analysis

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Authors: Mobina Sahraee Juybari

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There are around 300 languages spoken in Australia, and more than one-fifth of the population speaks a language other than English at home. The presence of international students in schools raises this number still further. Although the multilingual and multicultural status of Australia has been acknowledged by the government in education policy, the strong focus on English in institutional settings threatens the maintenance and learning of other languages. This is particularly true of universities’ language provisions. To cope with the financial impact of Covid-19, the government has cut funding for a number of Asian languages, such as Indonesian, Japanese and Chinese. This issue threats the maintenance of other languages in Australia and leaves students unprepared for the future job market. By taking account of the current reality of Australia’s diverse cultural and lingual makeup, this research intends to uncover the positioning of languages by having a historical look at Australia’s language policy and examining the value of languages and the probable impact of Covid-19 on the place of languages taught in Australian universities. A qualitative study will be adopted with language program tutors and course coordinators, with semi-structured interviews and government language policy analysis. This research hopes to provide insights into both the maintenance and learning of international language programs in tertiary language education in Australia and more widely.

Keywords: Australia, COVID-19, higher education sector, language maintenance, language and culture diversity

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Authors: Buket Celik Ünal, Onur Ünal

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Communication is the most challenging part of maintenance operations. Communication between expert and fieldworker is crucial for effective maintenance and this also affects the safety of the fieldworkers. To support a machine user in a remote collaborative physical task, both, a mobile and a stationary device are needed. Such a system is called a shared vision system and the system supports two people to solve a problem from different places. This system reduces the errors and provides a reliable support for qualified and less qualified users. Through this research, it was aimed to validate the effectiveness of using a shared vision system to facilitate communication between on-site workers and those issuing instructions regarding maintenance or inspection works over long distances. The system is designed with head-worn display which is called a shared vision system. As a part of this study, a substitute system is used and implemented by using a shared vision system for maintenance operation. The benefits of the use of a shared vision system are analyzed and results are adapted to the wind turbines to improve the occupational safety and health for maintenance technicians. The motivation for the research effort in this study can be summarized in the following research questions: -How can expert support technician over long distances during maintenance operation? -What are the advantages of using a shared vision system? Experience from the experiment shows that using a shared vision system is an advantage for both electrical and mechanical system failures. Results support that the shared vision system can be used for wind turbine maintenance and repair tasks. Because wind turbine generator/gearbox and the substitute system have similar failures. Electrical failures, such as voltage irregularities, wiring failures and mechanical failures, such as alignment, vibration, over-speed conditions are the common and similar failures for both. Furthermore, it was analyzed the effectiveness of the shared vision system by using a smart glasses in connection with the maintenance task performed by a substitute system under four different circumstances, namely by using a shared vision system, an audio communication, a smartphone and by yourself condition. A suitable method for determining dependencies between factors measured in Chi Square Test, and Chi Square Test for Independence measured for determining a relationship between two qualitative variables and finally Mann Whitney U Test is used to compare any two data sets. While based on this experiment, no relation was found between the results and the gender. Participants` responses confirmed that the shared vision system is efficient and helpful for maintenance operations. From the results of the research, there was a statistically significant difference in the average time taken by subjects on works using a shared vision system under the other conditions. Additionally, this study confirmed that a shared vision system provides reduction in time to diagnose and resolve maintenance issues, reduction in diagnosis errors, reduced travel costs for experts, and increased reliability in service.

Keywords: communication support, maintenance and inspection tasks, occupational health and safety, shared vision system

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Authors: Héctor Rivera-Gómez, Eva Selene Hernández-Gress, Oscar Montaño-Arango, Jose Ramon Corona-Armenta

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This research studies the joint production, maintenance and subcontracting control policy for an unreliable deteriorating manufacturing system. Production activities are controlled by a derivation of the Hedging Point Policy, and given that the system is subject to deterioration, it reduces progressively its capacity to satisfy product demand. Multiple deterioration effects are considered, reflected mainly in the quality of the parts produced and the reliability of the machine. Subcontracting is available as support to satisfy product demand; also overhaul maintenance can be conducted to reduce the effects of deterioration. The main objective of the research is to determine simultaneously the production, maintenance and subcontracting rate which minimize the total incurred cost. A stochastic dynamic programming model is developed and solved through a simulation-based approach composed of statistical analysis and optimization with the response surface methodology. The obtained results highlight the strong interactions between production, deterioration and quality which justify the development of an integrated model. A numerical example and a sensitivity analysis are presented to validate our results.

Keywords: subcontracting, optimal control, deterioration, simulation, production planning

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Authors: R. Nayak, T. Panwar, R. Padhye

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Protective textiles get soiled, stained and even worn during their use, which may not be usable after a certain period due to the loss of protective performance. They need regular cleaning and maintenance, which helps to extend the durability of the clothing, retains their useful properties and ensures that fresh clothing is ready to wear when needed. Generally, the cleaning processes used for various protective clothing include dry-cleaning (using solvents) or wet cleaning (using water). These cleaning processes can alter the fabric surface properties, dimensions, and physical, mechanical and performance properties. The technology of laundering and dry-cleaning has undergone several changes. Sustainable methods and products are available for faster, safer and improved cleaning of protective textiles. We performed a comprehensive and systematic review of green technologies and eco-friendly products for sustainable cleaning of protective textiles. Special emphasis is given on the care and maintenance procedures of protective textiles for protection from fire, bullets, chemical and other types of protective clothing.

Keywords: Sustainable cleaning, protective textiles, ecofriendly cleaning, ozone laundering, ultrasonic cleaning

Procedia PDF Downloads 238

Authors: Amandeep Kaur, Manmeet Gill

Abstract:

The climacteric is a wide phenomenon. Women of the entire world go through it at their own level. It’s a topic on which women hesitate to talk openly. It includes breast tenderness, uterine bleeding, arthralgia, hemorrhage, changes in emotional level such as facing depression, emotional breakdown, irritability and others. Other than such emotional breakdown nausea, vomiting, headache, gaining or losing weight is common problem associated with the climacteric disorder. The purpose of the present study is to assess the Climacteric disorders among women such as during menopause whatever a woman or girl faces mentally or physically. This is mainly done in women when they reached the age of 12 to 48 worldwide. For completing the study two objectives have been taken. The first objective of the study is to analyze the symptoms which lead to Climacteric among women such as Vaginal problems, Breast changes, Behavioral problems, Weight gain, Problems in the urinary tract etc. and the second Objective is to identify the variables which affect Climacteric these are Physical variables (lack of energy, joint soreness, stiffness, back pain etc.), Psychological variables (anxiety, poor memory, inability to concentrate) and Vasomotor variables (hormone estrogen fall, etc). The secondary source of method or data is used to deal with the theme of paper. Sometimes the word climacteric is interchanged with the term menopause and all these changes are high during the period of menopause among women.

Keywords: climacteric and their symposiums, disorder, reviews, in middle age

Procedia PDF Downloads 137

Authors: Kemal Efe Eseller, Göktuğ Yazici

Abstract:

Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

Procedia PDF Downloads 88

Authors: Mohammed Ali Faqeeh

Abstract:

SCADA System Alarms Optimization Process has been introduced recently and applied accordingly in different implemented stages. First, MODBUS communication protocols between RTU/SCADA were improved at the level of I/O points scanning intervals. Then, some of the technical issues related to manufacturing limitations were resolved. Afterward, another approach was followed to take a decision on the configured alarms database. So, a couple of meetings and workshops were held among all system stakeholders, which resulted in an agreement of disabling unnecessary (Diagnostic) alarms. Moreover, a leap forward step was taken to segregate the SCADA Operator Graphics in a way to show only process-related alarms while some other graphics will ensure the availability of field alarms related to maintenance and engineering purposes. This overall system management and optimization have resulted in a huge effective impact on all operations, maintenance, and engineering. It has reduced unneeded open tickets for maintenance crews which led to reduce the driven mileages accordingly. Also, this practice has shown a good impression on the operation reactions and response to the emergency situations as the SCADA operators can be staying much vigilant on the real alarms rather than gets distracted by noisy ones. SCADA System Alarms Optimization process has been executed utilizing all applicable in-house resources among engineering, maintenance, and operations crews. The methodology of the entire enhanced scopes is performed through various stages.

Keywords: SCADA, RTU Communication, alarm management system, SCADA alarms, Modbus, DNP protocol

Procedia PDF Downloads 166

Authors: Priscila M. Lima, Corine A. P. de Almeida, Muriele R. de Lima, Fernando J. C. Magalhães Filho

Abstract:

This work characterized the preliminary treatment in WWTPs in the state of Mato Grosso Do Sul (Brazil) and analyzed aspects of operation and maintenance of solid waste retained, and was evaluated the interference of this step in treatment efficiency beyond the relationship between solid waste generation with rainfall and seasonality in the region of each WTPs. The results shown that the standard setting in the preliminary treatment consists of grid along with Sand Trap, followed by Parshall that is used in 94.12% of WWTPs analyzed, and in 5.88% of WWTPs it was added the air-lift to the Sand Trap. Was concluded that the influence of rainfall, flow and seasonality associated with the rate of waste generation in the preliminary treatment, had little relation to the operation and maintenance of the primary treatment. But in some cases, precipitation data showed increased rainfall converging with increased flow and solid waste generation.

Keywords: pretreatment, sewage, solid waste, wastewater

Procedia PDF Downloads 471