Maintenance Engineering

In this paper, a Pareto multiobjective optimization is performed to design new luminaires, finding the optimal forward current, number of LEDs and proper heatsink by taking into account the cost, the energy consumption and the impact on the environment (life cycle analysis from cradle to grave). Three commercialized white LEDs have been studied and modelled in terms of optical, electrical, thermal and aging performances. The multiobjective methodology is also applied to other lamps (incandescent, halogen, fluorescent), indicating that LED lighting has a great potential of energy and cost savings with a minimization of environmental impacts on the long run.

The physical number of sensor nodes constitutes a major cost factor for Wireless Sensor Networks (WSN) deployments. Hence, a natural goal is to minimize the number of sensor nodes to be deployed, while still maintaining the desired properties of the WSN. However, sparse networks even while connected, usually suffer from topology irregularities that negatively impact the network lifetime and responsiveness, i.e., sensor data delivery reliability and latency. In addition, sensor node failures easily complicate/enforce/aggravate these irregularities. Valuable efforts have been conducted to discover topology specific anomalies such as coverage holes or critical/bottleneck nodes. Unfortunately, these efforts suffer from at least one of the following drawbacks: (a) They are centralized and consequently inefficient in largescale networks, (b) they are tailored to one class of anomalies, or (c) do not propose how to remedy the identified anomaly. In this paper, we focus on sparse WSN which usually show varied topology irregularities and propose an in-network and localized strategy that efficiently (i) discovers generic topology irregularities, and (ii) identifies locations for minimal number of new augmented sensor deployments to remedy topology irregularities and sustain the desired operational requirements. We show the effectiveness and efficiency of the solution through a set of extensive simulations.

The electric power industry requires qualified personnel to support an optimal and safe operation. Since its beginnings, the IIE has been developing different training technologies and systems for CFE, the main utility for generation, transmission and distribution of electric power in Mexico. Some of these endeavors provide tailored instruction considering trainees traits such as learning styles, affective states and current knowledge. Other developments are focused on enabling multifunctionality. The IIE has also developed intelligent assistant systems, virtual reality systems, and power plant simulators. Besides this, the IIE is interested in developing e-learning platforms to support CFE's personnel training. This paper presents a summary of these developments.

A Ground Fault Circuit Interrupter (GFCI) is a safety device that prevents electric shock by using fundamental electromagnetic principles. Its operation centers on a sensor, a toroidal iron core, that monitors the current in the "hot" and "neutral" wires. In a normal circuit, these two currents are perfectly equal and opposite, creating magnetic fields that cancel each other out, resulting in zero net magnetic flux in the core. When a dangerous ground fault occurs, some current "leaks," creating an imbalance. This imbalance produces a net changing magnetic flux, which, according to Faraday's Law of Induction, induces a small voltage in a sensing coil. A quantitative model shows that a hazardous 5 mA fault current generates a detectable peak voltage of about 1.33 millivolts, which is then amplified to trip a relay and cut off the power in a fraction of a second.

El propósito de esta investigación consistió en analizar los parámetros de mantenimiento de confiabilidad, disponibilidad y mantenibilidad (CDM) de la planta compresora Jusepín 12 situada en el estado Monagas de Venezuela. En el inicio del estudio, se detectaron 991 fallas durante el periodo 2017 -2019, de las cuales el 84,3 % se atribuyeron al sistema motor compresor, lo que representó un total de 17166,5 horas fuera de servicio de este sistema. Por lo tanto, la investigación se enfocó primordialmente en este sistema. Además, en el intervalo temporal previo y basándose en los tiempos entre fallos (TBF), ajustados a la distribución Weibull 3P, se registraron valores de confiabilidad del 43,75 %; 36,44 % y 37,81 %. Estos valores se sitúan en 95%, valor por debajo del Indicador Clave de Rendimiento (KPI) para sistemas motocompresores. Con respecto a la mantenibilidad, los valores de 71,85 %; 72,46 % y 71,42 % fueron considerados aceptables. Finalmente, la disponibilidad del sistema investigado fue del 79,4 %; 86,4 % y 84,8%; cifras que se sitúan por debajo del KPI establecido (94 %). Se concluyó que la disponibilidad obtenida se debe a una mejor mantenibilidad en comparación con la confiabilidad.

This paper deals with a tool which may help maintenance manager to schedule maintenance activities. To help him, we show that by using events which can be observed on a process, like maintenance events, we can predict failures before they occur. Principles are based on the hypothesis that failure is preceded by a typical sequence of events. We also show that Hidden Markov Models can be used according to a good choice of parameters.

Smart manufacturing depends on stable tribological interfaces. This systematic review synthesizes how Advanced Lubrication Management Systems (ALMS) affect equipment longevity and operational efficiency by integrating online oil and film-state sensing, automated dosing and filtration, contamination control, and connections to CMMS, MES, and digital twins. Following PRISMA 2020, we searched databases for records from 2010 to 2025, screened against predefined criteria, and appraised quality. In total, 115 studies met inclusion and we extracted outcomes for reliability, efficiency, energy, and fluids. Across 64 reliability-reporting papers, median gains were notable: mean time between failures increased by 19 percent and failure or hazard rates declined by 23 percent, underpinned by typical two-step improvements in ISO 4406 cleanliness codes and 35 percent lower moisture. Operational performance improved across 74 studies: Overall Equipment Effectiveness rose by 5.6 points, driven by availability (+3.4), with additional gains in performance (+1.7) and quality (+0.5); unplanned downtime fell by 27 percent and mean time to repair by 14 percent. Energy intensity decreased by 8.5 percent and by 12 percent in dip or splash-lubricated gearboxes; lubricant consumption fell by 22 percent and drain intervals extended by 35 percent. Economic reporting indicated a 13 month payback and first-year return of 86 percent. Moderator analyses show larger, steadier effects when ALMS are closed-loop and digitally integrated, when cleanliness and moisture targets are sustained, and when oil-state sensing is paired with contactlevel regime detection. Findings position lubrication as a governed, data-driven control problem that advances reliability, throughput, energy efficiency, and sustainability in smart manufacturing.

This paper addresses the problem of tracking a non maneuvering target in a maritime surveillance context, under emission constraint. In such a situation, the onboard radar shall be used as few as possible, in order not to be detected. To maintain the tactical situation, it is convenient to take advantage of other non emissive sensors such as ESM. However, we have to jointly face the problem of nonlinear filtering and the lack of observability due to bearing-only measurement. This paper proposes to compare the performances of two algorithms based on Extended Kalman Filter : Cartesian coordinates and Modified Polar coordinates. Simulation results demonstrate that the latter is well adapted to long mission durations where radar is almost not used, while the former gets significantly more inaccurate.

This study investigates the implementation of programmable logic controllers (PLCs) in automated urban water distribution systems, focusing on their role in leakage mitigation and operational efficiency. Utilizing a mixed-methods approach with experimental and observational data from SCADA-integrated field studies, the research demonstrates that PLC adoption reduces leakage rates by an average of 25%, with locations implementing comprehensive PLC frameworks achieving reductions of up to 40%. These improvements are attributed to enhanced real-time monitoring, automated control, and rapid leak response facilitated by PLC systems, underscoring their effectiveness compared to traditional water management approaches. The study also highlights the potential integration of IoT sensors and AI-driven analytics with PLC systems to further advance smart water distribution management. Contextual factors, including infrastructure variability and regulatory environments, are identified as influential in determining PLC effectiveness, indicating the need for adaptive strategies in diverse urban settings. The findings advocate for municipalities to consider PLC-based automation in water management not only to optimize resource allocation and reduce operational costs, but also to strengthen sustainability and resilience in urban water supply chains.

An efficient maintenance schedule for gas turbines of power plants ensures a reliable electricity supply. This study addresses a generator maintenance scheduling problem arising from Taiwan's combined cycle power plant with two notable characteristics, i.e., a specific sequence of various maintenance types and the concept of performing maintenance according to operational hours spent. The objective is to minimize the total maintenance cost. The problem is formulated as a Mixed Integer Linear Program which is solvable by an off-the-shelf exact solver, i.e., CPLEX. Moreover, a set of newly generated instances is proposed as benchmark instances for the problem. The instances generated are based on the realistic conditions obtained from the historical record of Taiwan's combined cycle power plant. Computational studies are presented as interesting insights regarding the complexity of the problem and the factors driving the total maintenance costs.

A new methodology is presented, allowing to evaluate protection systems reliability and availability indexes, taking into account equipment failure and repair rates. The protection scheme is translated into a reliability graph, enabling to identify all possible logical occurrences in the system, either meaning failure or successful operation. Evaluation of the system reliability and availability indexes is achieved by Monte Carlo simulation. The methodology is applied to a typical transmission line protection bay, for different time to repair scenarios, and conclusions on asset management options are drawn.

Service providers are experiencing a challenge that is caused by the explosive growth in demand for high-speed connections to and across metropolitan, regional and core networks. Operators are under pressure to increase the capacity of their networks to meet these ever growing requirements. To improve service agility and network efficiency and reduce costs throughout their entire infrastructures, providers are evaluating ways to use packet transport everywhere. Carrier Ethernet is a new mechanism that is being proposed to consolidate the packet optical transport network. Multiprotocol Label Switching-Transport Profile (MPLS-TP) -a variant of Carrier Ethernet offers a potentially important addition within the packet transport toolset. Operations, Administration, and Maintenance (OAM) functionality is central to this new standard. This helps service creation and assurance thus providing an extension to packet transport capabilities within emerging multi-layer infrastructure architectures. This paper aims at exploring the performance of MPLS-TP network using an extensive simulation model.

The paper deals with a specific subsystem analysis called butt shot blast station of an Aluminium plant, as the subsystem performance contribute to the entire functioning of the plant. Six years' maintenance data on failures, repairs and various associated costs are collected for the purpose of this analysis. Three types of maintenances noted for the subsystem viz., corrective maintenance, inspection as proactive maintenance and service on requirement. Measures of subsystem effectiveness such as mean time to subsystem failure, availability of the system, busy period analysis and expected number of visit by the repairman for repair have been obtained. Semi-Markov processes and regenerative point techniques are used in this analysis.

This paper deals with the systemic design of permanent magnet (PM) generator associated with fixed-pitch turbine for tidal energy generation. The main problem with marine current turbines systems are the maintenance requirements of the drive-train. It is known that the blade pitch system increases the complexity, cost, and the maintenance requirements of the drive-train. In offshore energy generation, the maintenance should be minimized as much as possible. For that purpose direct-drive permanent-magnet machines associated with fixed-pitch marine turbines can be an attractive solution. The main challenge with fixed-pitch blades is to ensure the power limitation of the turbine at high speed without using variable pitch system. As solution, we propose a systemic design approach of the generator that takes into account the power limitation requirement. In addition, this methodology takes into account the tidal site energy potential, the turbine characteristic, the control strategy, the generator specifications and the power converter constraints. The obtained results show the feasibility of our approach to satisfy the control strategy requirements.

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

Dynamic data-driven applications systems (DDDAS)-based methods entail models which at execution time that can dynamically incorporate measurement data and in reverse, the model controls the instrumentation to target measurements or apply coordinated actuation, and do so by combining the use of computer models, mathematical algorithms, and measurements systems to work with dynamic systems. Large-scale dynamic systems experience stochastic forces over space and time that often lead to component malfunctioning or failures which result in costly repairs. Critical infrastructures, such as wind-turbine farms, are large-scale and complex systems that go through highly dynamic operations which at the same time involve uncertainties in condition characterization. In many instances, the information required to make operational decisions is not available in advance, rather is revealed as time progresses. Online optimization is a suitable framework for problems involving data uncertainties that evolve over time, requiring important and cognizant system decisions to be made sequentially prior to observing the entire data stream. An important challenge in using online control is to optimize the decision parameters. To address wind-turbine operations, a simulation-based online dynamic data-driven framework (SODDF) is presented which provides a way to obtain high quality operational decisions when the system has too many parameters and where traditional means to optimize the outcomes are intractable. The DDDAS framework is applied to improve maintenance scheduling in wind-turbine farm systems using a discrete event system specification wind-turbine farm simulation model (DEVS-WFSM).

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Recently, Prognostics and Health Management (PHM) has emerged to promote predictive maintenance as a methodological key to overcome the limitations of traditional reliability analysis. The Natural Language Processing (NLP) methods allow the maintenance log usage for maintenance diagnostics and decision making. The Maintenance Work Orders (MWOs) contain vital health indicators and decades of experience related to various maintenance actions. However, due to the unstructured nature of maintenance text, it is not common to develop a tool using these textual maintenance entries. This paper proposes a textual Case-Based Reasoning (CBR) approach combined with Technical Language Processing (TLP) to find solutions for new problems based on previous experiences. The Bidirectional Encoder Representations from Transformers (BERT) model is adopted for maintenance data using unsupervised fine-tuning technique Transformer-based Sequential Denoising Auto-Encoder (TSDAE) for aviation case study. Results show that the pre-trained BERT model can adopt domain-specific data and produce semantic matches with only a small amount (1000 samples) of domain specific data.

Human bone-marrow mesenchymal stem cells (BM MSCs) and adipose derived stem cells (ADSCs) were evaluated for evidence of senescence during exposure to hypoxic conditions and protracted ex vivo expansion. Under hypoxia we have observed microsatellite instability and down-regulation of genes involved in DNA repair. Simultaneously, a decrease in ATP content and in the number of mitochondrial genomes was observed. Irrespective of O 2 tension or cell passage, we observed several heteroplasmic point mutations, some of which have been reported in a wide range of tumors. These results alert for a careful analysis of cell integrity during the development and application of regenerative therapies.

The growing complexity of systems and the need for executing large projects have led to the development of complex fl exible manufacturing systems (FMS) demanding specifi c control-monitoring architectures. The problem of failure occurrence tends

This contribution presents the results of an electrical motor on-line diagnosis. The implementation of a dynamic diagnostic decision is interesting in the case of closedloop systems for which the residual responses may be transient. A neural network cl assifier is developed to diagnose the sensor and the actuator faults from some residuals. Simulation results are presented to show the detection and isolation ability . The classifier has been designed to cope with transient behaviours of the residuals generated by abrupt faults. The robustness with respect to unknown input (torque disturbance) and the sensitivity with respect to drift faults are experimented.

A new architecture is proposed for the first manned Mars mission, based on current NASA developments (SLS and Orion), chemical propulsion for interplanetary transit, aerocapture for all vehicles, a split strategy, and a long stay on the surface. Two important choices make this architecture affordable and appropriate for the first mission. The first is splitting the Earth return vehicle into two parts that are launched separately and dock in Mars orbit. This is necessary to make aerocapture feasible and efficient, which considerably reduces mass. The second is reducing the crew to 3 astronauts. This simplifies the mission and reduces the SLS payload mass under the 45-metric ton limit for a direct TMI (trans-Mars injection) burn without LEO assembly. Only 4 SLS launches are required. The first takes the Mars ascent vehicle and in situ resource utilization systems to the planet's surface. The second takes the first part of the Earth return vehicle, the habitat, into Mars orbit. Two years later, two further SLS launches take a dual-use habitat (outbound trip and surface), Orion, and an enhanced service module to LEO, and then into Mars orbit, followed by the landing of the habitat on the surface. Transit time is demonstrated to be easily reduced to less than 6 months, with relatively low impact on propellant mass and none at all on the architecture.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

A heavy version of the Ariane launcher is hypothesized for a manned mission to Mars. This enhanced Ariane has 100 mt LEO capability and 36 mt capability for transMars injection (V∞=3.5km/s). In order to simplify the scenario and minimize the costs, it is proposed a pre-deploy semi-direct architecture with several rather small spaceships, 3astronauts and an aerocapture maneuver for Mars orbit insertion. There are several advantages: First, as the payload to the Mars surface is split into equal parts, the same landing space vehicle can be used with mass and size compatible with the payload capability of the launcher. Second, the choice of relatively small landers allows the use of simple deployable rigid heatshields, which could be used both for aerocapture and entry, descent and landing. The use of small landers also reduces the complexity of the tests for the qualification of the descent and landing systems and procedures, which is a critical aspect of the preparation phase. 5 launc...

It has been suggested that the design of the last NASA reference mission for the human exploration of Mars is lacking sufficient considerations for human factors and human machine interactions. The NASA team examined many different options, long or short stay, chemical or nuclear thermal propulsion, pre-deploy or all-up, in situ resource utilization (ISRU) or not, etc. The decision process was based on a bottom-up approach, which led to local optimizations but to unpractical solutions in certain domains. For instance, the optimal number of astronauts has been determined according to skills requirements and organizational issues but no attention has been paid to its impact on the payload, on the mass of the landers, on the volume of the habitat and on the overall risks of the mission. A human centered design approach is proposed here, with a particular focus on interdependencies and human systems interactions. Following the guiding principles of human rated space systems, it is suggested that different choices may be more appropriate. The main ones are a reduction of the size of the crew, the entire duplication of the mission and a trade-off between "pre-deploy" and "all-up".

We present in this paper a new family of algorithms for regular languages inference from complete presentation. Every algorithm of this family, on input of the sets of words (D+, D-), obtains for every x in D+ at least a non deterministic finite automaton (NFA) which accepts x and is consistent with D-. This automaton is, besides, irreducible in the sense that any further merging of states accepts words of D-. The output of the algorithm is a NFA which consists of the collection of NFAs associated to each word of D+. Every algorithm of the family converges to a automaton for the target language. We also present the experiments done to compare one of the algorithms of the family with two other well known algorithms for the same task. The results obtained by our algorithm are better, both in error rate as in the size of the output.

The maintenance of plant in the electric power stations involves an annual expenditure of millions of dollars and it is therefore essential to establish the cost-effectiveness of the present maintenance policies. The major problems in this evaluation are the lack of information and lack of suitable techniques to analyze the information. Most items of electric power plants are complex mechanical units and as such it is extremely difficult to predict their failure although condition monitoring can sometimes be used. This study explores how to use reliability engineering to identify best practice for maintenance work in electric power stations. It identified how to identify failure before their occurrences and how the maintenance management functions can become more conscious of the sole of maintenance management in enhancing has become the prominent management issues for electric utilities.

Service Dependability has been recognized in the transportation literature as an effective measure of transit systems quality. Dependability models link this measure of service quality with Reliability, Availability and Maintainability characteristics of the subsystems operating in the transport system. Purpose of this paper is to present a dependability model for the analysis of the impact of power electronics reliability on the Service Dependability in the case of a vehicle for High Speed railway applications.

Automated traceability applies information-retrieval techniques to generate candidate links, sharply reducing the effort of manual approaches to build and maintain a requirements trace matrix as well as providing after-the-fact traceability in legacy documents.The authors describe nine best practices for implementing effective automated traceability. equirements traceability, defined as the ability to "follow the life of a requirement in both a forward and backward direction," 1 provides critical support for software engineers as they develop and maintain software systems. Traceability helps determine that researchers have refined requirements into lower-level design components, built them into the executable system, and tested them effectively. It further helps analysts understand the implications of a proposed change and ensures that no extraneous code exists. Numerous standards include traceability as a recommended or legally required activity. For example, IEEE Std. 830-1998 states that a software requirements specification must be traceable. The standard defines an SRS as traceable "if the origin of each of its requirements is clear and if it facilitates the referencing of each requirement in future development or enhancement documentation." Backward traceability is required from requirements to "previous stages of development," while forward traceability proceeds from requirements to "all documents spawned by the SRS." 2 Further, organizations building safety-critical systems are often legally required to demonstrate that all parts of the code trace back to valid requirements. Laws such as the US Sarbanes-Oxley Act of 2002 require organizations to implement changemanagement processes with explicit traceability coverage for any parts of a software product that potentially impact the balance sheet. Unfortunately, many organizations fail to implement effective traceability practices either due to difficulties in creating, assessing, using, and maintaining traceability links or because they succumb to the misconception that traceability practices return little value for the effort involved. Traditionally, traceability links are physically stored in spreadsheets, text files, databases, or requirements management (RM) tools such as Telelogic's DOORS or IBM's Rational RequisitePro, and such links tend to deteriorate during a project as time-pressured team members fail to update them. 1 Offshoring and outsourcing exacerbate this problem by creating temporal and physical distance between subjectmatter experts and developers. Because manual traces are often created in an ad hoc fashion according to the developers' whim, they tend to be inconsistent and often incomplete. Current RM tools provide limited support for link creation and maintenance, offering features

High operations and maintenance costs for wind turbines reduce their overall cost effectiveness. One of the biggest drivers of maintenance cost is unscheduled maintenance due to unexpected failures. Continuous monitoring of wind turbine health using automated failure detection algorithms can improve turbine reliability and reduce maintenance costs by detecting failures before they reach a catastrophic stage and by eliminating unnecessary scheduled maintenance. A SCADAdata based condition monitoring system uses data already collected at the wind turbine controller. It is a cost-effective way to monitor wind turbines for early warning of failures and performance issues. In this paper, we describe our exploration of existing wind turbine SCADA data for development of fault detection and diagnostic techniques for wind turbines. We used a number of measurements to develop anomaly detection algorithms and investigated classification techniques using clustering algorithms and principal components analysis for capturing fault signatures. Anomalous signatures due to a reported gearbox failure are identified from a set of original measurements including rotor speeds and produced power.

With global cuts in defense budgets, air forces have to sustain the same level of readiness with a reduced number of aircraft. To succeed with this challenge, it is not sufficient to improve current maintenance concepts, but also new ones have to be introduced. Traditionally, the development of on-board functions and maintenance concepts has been performed rather independently. The new approach is to focus on an integration of these two developments and to adapt a life cycle view together with a disregard of organizational boundaries. To facilitate necessary changes, a study is performed by Saab and Luleå University of Technology. The study focuses on the possibilities to integrate RCM and PHM in a cost-effective way. The aim is to reduce the workload of maintenance development and to find out how to combine RCM and PHM to achieve a joint development of the aircraft and its maintenance program throughout the aircraft's whole life cycle. The paper describes some similarities and differences between RCM and PHM. Furthermore, the paper describes some aspects of how RCM and PHM are complementing each other and what kind of adaptations that has to be done to achieve a successful integration. For example, a good application of RCM early in system design might generate a pull for PHM-technology integration and motivate design changes of the aircraft. Simultaneously, available PHMtechnologies might strongly affect the selection of applicable and effective maintenance tasks in the development of the initial maintenance program. PHM also gives a direct feedback of the maintenance program's performance. In addition, an approach for a stepwise integration of RCM and PHM is outlined. 12 TABLE OF CONTENTS 1. INTRODUCTION .

Results of researches in different areas of science and technique confirm that an effective route in order to solve important tasks is the possibility of migrating data resource within the cloud. Therefore Cloud Computing services including Infrastructure as a Service Cloud (IaaS Cloud) should have high availability level. It is serious problem, because even large cloud providers face with sudden failures of IaaS Cloud. It comes no surprise, that scientists consider taxonomy of this system on base of using overall underlying components, such as physical machines (PMs) and virtual machines (VMs). However cloud providers should always remember that hidden failures of PMs are one of the main causes of damage for their cloud assets. In this paper we propose approach on base of using Semi-Markov model in order to determine availability level for the IaaS Cloud with Technical State Control System.

The transition to Industry 4.0 has improved factories by improving the manufacturing process. With increasing automation, awareness of the role of humans in Industry 4.0 maintenance management is also important for the realization of the Industrial Internet of Things (IIoT). Today's smart factories use data from various sources to extract valuable insights that improve manufacturing processes and avoid failures. These improvements also add to the complexity of resolving different maintenance issues faced during the manufacturing process. In this context, there is a need to leverage untapped human knowledge in Maintenance Work Orders (MWOs) to handle these complex challenges using state-of-the-art Natural Language Processing (NLP) techniques. More precisely, the development of Industry 4.0 technologies is leading to a growing interest in using digital twins in many sectors. Digital twin-based services are revolutionizing design, manufacturing, product use, and maintenance (diagnosis, prognosis, and decision-making). This paper proposes a human knowledge centered intelligent maintenance decision support. The proposed service can find solutions to new maintenance problems using knowledge in past maintenance records in a digital twin environment. The architecture of the proposed service and its connections with Physical Space (PS), Virtual Space (VS) and Digital Twin Data (DTD) are presented in this paper. The performance of the service is validated using a case study on an open-source dataset of real MWOs from mining excavators. Results indicate that state-ofthe-art NLP techniques can be used to process human knowledge in MWOs and generates interesting patterns. This study is also a step forward towards application of Technical Language Processing (TLP) in a smart manufacturing setup.

In the dynamic energy sphere in the U.S., operational dependability of gas turbine engines becomes vital to the continuous power production and sustenance of the national grid. Peak and baseload power production, the mainstay of gas turbines, is exposed to high thermal, mechanical, and chemical stresses that lead to wear out of components over time. Traditionally, maintenance practices have either been reactive or time-based, based upon fixed intervals, which has led to premature replacement of components, or vice versa, undetected degradation leading to catastrophic failure. Such approaches carry high operating costs, lower turbine availability, and jeopardize plant safety. The central objective of this research is to build and test an AI-driven fault detection system to identify early signs of failure in gas turbine engines and apply it specifically to deployment within the energy sector of the U.S. Improving gas turbine performance is essential to raising the cost-effectiveness and sustainability of power production systems. The dataset used in this research entails high-resolution operational parameters gathered from different industrial gas turbines operating within U.S. energy facilities. Engine operational data includes multiple time-dependent measurements that monitor essential parameters like turbine temperature at the inlet and outlet and rotational speed, torque measurements alongside vibration levels and power output, fuel rate and intake pressure, and exhaust temperature and oil temperature. Real-time data acquisition from embedded sensor arrays allowed researchers to track turbine performance throughout changing operational states at sub-minute data points. A centralized time-stamping system maintains channel synchronization, thus allowing analysts to draw accurate conclusions about operational states throughout the recorded period. To support strong and interpretable fault detection, we utilized a variety of machine learning models, each chosen for its specific strength at discriminating operations from fault conditions in gas turbines. We used a multi-metric-based evaluation approach that combined statistical validity with operational applicability to assess each model's fault detection capability. Logistic Regression attained the highest accuracy, followed very closely by Random Forest. XG-Boost attained the lowest accuracy of all three algorithms. The use of AI-driven fault detection under predictive maintenance has the potential to revolutionize U.S. power plants using gas turbines as the primary source of electricity generation. In the fiercely competitive and heavily regulated environment of the U.S. energy industry, fault anticipation presents the key to competitive advantage. Using AI-based diagnostics reduces manual checks and simplifies the servicing process by prioritizing technician resources to confirmed at-risk components. AI-powered fault detection is critical to improving grid resilience within the U.S. energy infrastructure by assuring that peak-demand-balancing and grid-stabilizing gas turbines remain fault-free.

Dans ce travail nous avons etudie analytiquement et numeriquement le modele de remplacement base sur l’âge, c'est-a-dire que l’âge de chaque piece est connu et on change la piece des que son âge atteint cette valeur. Une etude analytique realisee dans le cas d’une loi de Weibull, a permis de resoudre les equations differentielles resultantes sous certaines conditions mathematiques. Ensuite ces equations sont resolues numeriquement pour les differents parametres de ce probleme qui sont le rapport des couts de maintenance, le parametre d’echelle et le parametre de forme. Les resultats obtenus ont ete analyses et discutes ; leurs applications a des cas reelles peut fournir au service de maintenance un element clef pour choisir la periode la plus appropriee pour effectuer la maintenance preventive au cout minimum.

This systematic review explores the integration of artificial intelligence (AI) in predictive maintenance and condition monitoring systems, with a focus on smart diagnostics across industrial applications. The study examines how AI technologies-including machine learning, deep learning, and hybrid models-are transforming traditional maintenance strategies by enabling automated fault detection, anomaly classification, and remaining useful life (RUL) estimation based on realtime sensor data. Drawing on a comprehensive review of 156 peer-reviewed articles published between 2015 and 2025, the research synthesizes current advancements in AI-driven maintenance tools and their applications in diverse industrial sectors such as manufacturing, energy, transportation, and utilities. Special emphasis is placed on the role of sensor technologies, including vibration, acoustic, thermal, and ultrasonic sensors, in providing the high-frequency data necessary for effective AI integration. Furthermore, the study discusses key theoretical underpinningsincluding Reliability-Centered Maintenance (RCM), Prognostics and Health Management (PHM), Spatial Decision Support Systems (SDSS), and Information Systems Success Models-that frame the adoption and impact of smart diagnostics. Despite clear benefits, challenges such as data quality, model interpretability, and integration complexity remain persistent. This review contributes to both academic discourse and industrial practice by offering a comprehensive understanding of how AI technologies are redefining maintenance strategies and by identifying research gaps and opportunities for future exploration.

This paper presents a novel system for accurately mapping military targets using a combination of trigonometry and computer vision, deployed through unmanned aerial vehicles (UAVs). The proposed system integrates a drone equipped with a camera and employes trigonometric principles to calculate target distances based on the drone's position, angles and elevation. Upon sighting a target, the system utilizes computer vision algorithms to precisely identify and localize it within the drone's field of view. Subsequently, leveraging the calculated distances and the drone's GPS coordinates, the system accurately maps the target's coordinates on a global positioning system (GPS). The integration of trigonometry and computer vision enhances target localization capabilities, facilitating efficient and precise mapping of military objectives in diverse operational environments. This system holds significant potential for enchancig situational awareness and operational effectiveness in military applications.

This paper presents a novel system for accurately mapping military targets using a combination of trigonometry and computer vision, deployed through unmanned aerial vehicles (UAVs). The proposed system integrates a drone equipped with a camera and employes trigonometric principles to calculate target distances based on the drone's position, angles and elevation. Upon sighting a target, the system utilizes computer vision algorithms to precisely identify and localize it within the drone's field of view. Subsequently, leveraging the calculated distances and the drone's GPS coordinates, the system accurately maps the target's coordinates on a global positioning system (GPS). The integration of trigonometry and computer vision enhances target localization capabilities, facilitating efficient and precise mapping of military objectives in diverse operational environments. This system holds significant potential for enchancig situational awareness and operational effectiveness in military applications.

This paper presents a framework to support transparent, live migration of virtual GPU accelerators in a virtualized execution environment. Migration is a critical capability in such environments because it provides support for fault tolerance, ondemand system maintenance, resource management, and load balancing in the mapping of virtual to physical GPUs. Techniques to increase responsiveness and reduce migration overhead are explored. The system is evaluated by using four application kernels and is demonstrated to provide low migration overheads. Through transparent load balancing, our system provides a speedup of 1.7 to 1.9 for three of the four application kernels.

Owing to numerous potential applications, wireless sensor networks have been attracting significant research effort recently. The critical challenge that wireless sensor networks often face is to sustain long-term operation on limited battery energy. Coverage maintenance schemes can effectively prolong network lifetime by selecting and employing a subset of sensors in the network to provide sufficient sensing coverage over a target region. We envision future wireless sensor networks composed of a vast number of miniaturized sensors in exceedingly high density. Therefore, the key issue of coverage maintenance for future sensor networks is the scalability to sensor deployment density. In this paper, we propose a novel coverage maintenance scheme, scalable coverage maintenance (SCOM), which is scalable to sensor deployment density in terms of communication overhead (i.e., number of transmitted and received beacons) and computational complexity (i.e., time and space complexity). In addition, SCOM achieves high energy efficiency and load balancing over different sensors. We have validated our claims through both analysis and simulations.

In a previous paper, a new scenario for human missions to Mars has been presented. It can be named the 2-4-2 concept. The idea is to minimize IMLEO and the payload on Mars with a crew of only two astronauts. There are two astronauts in each rocket at departure from Earth. They meet on Mars to form a team of four and the pairs are reconstituted for the return (2-4-2). In the previous study it was proposed an assembly in LEO. We propose now a different approach with several small vehicles sent to Mars at the same time. There is no need for LEO assembly and the landers are lighter thus facilitating entry descent and landing on mars.

This paper focuses on off-line dimensioning offlexgrid networks. As any operator wishes to satisfy all trafficdemands characterized by a traffic matrix, considering blockingas an optimization objective is not appropriate. We thus considertwo other objectives, one of them related to path lengths and theother related to spectrum occupancy. We also define several indi-cators to characterize a mapping of demands on a given network,including some which are related to the remaining capacity ofthe network. We apply off-line dimensioning algorithms with thetwo different optimization objectives on a traffic matrix to besupported by a small flexgrid network and assess the impact ofthe selected optimization objective on the network level metricsin order to provide insight on how such an off-line dimensioningshould be performed.

This report summarizes the status of the computer program RAD-POW. RADPOW is a program for Reliability Assessment of electrical Distribution POWer systems. It was developed at KTH School of Electrical Engineering, within the research program EKC and the research project on reliability of new electrical distribution systems. Further on, RADPOW has been used and further developed within the RCAM research group at KTH School of Electrical Engineering. This status report contains a brief description of the RADPOW_2006 version, the Loadow module from the RADPOW_1999_PH version and a description of the work done in the resulting RADPOW_2007 version. This version now includes a tested load ow module and the ability to calculate the latest component importance indices developed within the RCAM research group. The source code for the program has also been restructured and commented in a more detailed level than before.

As we know that one of the most important equipment in power system, transformer plays a major role for stable operation of the power system. The transformer working under high and medium voltage network influences various stresses under working condition. As the ageing processes or minor faults on the transformer can cause the core deformation which leads to change the mechanical and electrical properties of the transformer. Sweep frequency response analysis (SFRA) is used as a tool for detection of core deformation or mechanical deterioration in transformer which is due to large electromechanical forces occurring from the fault currents or due to transportation and relocation of transformer. In this method, the frequency responses of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformer, this response is not possible. So Cross Correlation Co-Efficient (...

In this paper, we present the implementation issues of a virtual backbone that supports the operations of the Uniform Quorum System (UQS) and the Randomized Database Group (RDG) mobility management schemes in an ad hoc network. The virtual backbone comprises nodes that are dynamically selected to contain databases that store the location information of the network nodes. Together with the UQS and RDG schemes, the virtual backbone allows both dynamic database residence and dynamic database access, which provide high degree of location data availability and reliability. We introduce a Distributed Database Coverage Heuristic (DDCH), which is equivalent to the centralized greedy algorithm for virtual backbone generation, but only requires local information exchange and local computation. We show how DDCH can be employed to dynamically maintain the structure of the virtual backbone, along with database merging, as the network topology changes. We also provide means to maintain connectivity among the virtual backbone nodes. We discuss optimization issues of DDCH through simulations. Simulation results suggest that the cost of ad hoc mobility management with a virtual backbone can be far below that of the conventional link-state routing.