Relevant bibliographies by topics / Remainiing useful Life

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Remainiing useful Life'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Remainiing useful Life"

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Ahmadzadeh, Farzaneh, and Jan Lundberg. "Remaining useful life estimation: review." International Journal of System Assurance Engineering and Management 5, no. 4 (September 26, 2013): 461–74. http://dx.doi.org/10.1007/s13198-013-0195-0.

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Bechhoefer, Eric, and Marc Dube. "Contending Remaining Useful Life Algorithms." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1274.

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Operational readiness, reliability and safety are all enhanced through condition monitoring. That said, for many assets, there is still a need for a prognostic capability to calculate remaining useful life (RUL). RUL allows operation and maintenance personnel to better schedule assets, and logisticians to order long lead time part to help improve balance of plant/asset availability. While a number of RUL techniques have been reported, we have focused on fatigue crack growth models (as opposed to physics or deep learning of based models). This paper compares the performance of stress intensity models (linear elastic model, e.g. Paris’ Law), to Head’s theory (geomatical similarity hypothesis) and to Dislocation/Energy theories of crack growth. It will be shown that these models differ mainly in the crack growth exponent, and that this leads to large differences in the estimation of RUL during early state fault propagation, though the results of all three models converge as the RUL is shorted.
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Johansson, Carl-Anders, Victor Simon, and Diego Galar. "Context Driven Remaining Useful Life Estimation." Procedia CIRP 22 (2014): 181–85. http://dx.doi.org/10.1016/j.procir.2014.07.129.

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Vaidya, P., and M. Rausand. "Remaining useful life, technical health, and life extension." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 225, no. 2 (June 2011): 219–31. http://dx.doi.org/10.1177/1748007810394557.

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Pagitsch, Michael, Georg Jacobs, and Dennis Bosse. "Remaining Useful Life Determination for Wind Turbines." Journal of Physics: Conference Series 1452 (January 2020): 012052. http://dx.doi.org/10.1088/1742-6596/1452/1/012052.

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Banjevic, Dragan. "Remaining useful life in theory and practice." Metrika 69, no. 2-3 (December 4, 2008): 337–49. http://dx.doi.org/10.1007/s00184-008-0220-5.

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Murali Krishna, K., and Dr K. Janardhan Reddy. "Remaining useful life estimation of a Product." Journal of Physics: Conference Series 1716 (December 2020): 012028. http://dx.doi.org/10.1088/1742-6596/1716/1/012028.

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Agrawal, Shaashwat, Sagnik Sarkar, Gautam Srivastava, Praveen Kumar Reddy Maddikunta, and Thippa Reddy Gadekallu. "Genetically optimized prediction of remaining useful life." Sustainable Computing: Informatics and Systems 31 (September 2021): 100565. http://dx.doi.org/10.1016/j.suscom.2021.100565.

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Stevanović, Dragan, Aleksandar Janjić, and Dragan Tasić. "Methodology for circuit breakers remaining useful life assessment." Tehnika 74, no. 5 (2019): 687–93. http://dx.doi.org/10.5937/tehnika1905687s.

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Li, Min, Jiong Jiong Zhu, and Bin Long. "Particle Filter Approach for IGBT Remaining Useful Life." Advanced Materials Research 981 (July 2014): 86–89. http://dx.doi.org/10.4028/www.scientific.net/amr.981.86.

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With the increasingly widespread application, the requirement for PHM of IGBT is becoming gradually urgent. Based on particle filter theory, a method for remaining useful life (RUL) prediction of IGBT is proposed. Firstly, the deterioration parameters on-state VCE and ICE are extracted by temperature cycling test, then a model is developed based on the degradation trend exhibited by deterioration parameters. In the end, PF approach is applied to the IGBT's RUL prediction with the mentioned model. The results show that the proposed prediction method can achieve high prediction accuracy.
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Dissertations / Theses on the topic "Remainiing useful Life"

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Agatensi, Luca. "Studio e Sperimentazione su Manutenzione Predittiva in ambito Manifatturiero." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20352/.

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La Manutenzione Predittiva - PdM è una pratica che viene utilizzata per ottimizzare i piani di manutenzione delle risorse, attraverso la previsione di guasti alle stesse, con tecniche che sfruttano la predizione di comportamenti sulla base di dati passati. L'applicazione di PdM può portare numerosi vantaggi alle aziende, tra cui, la riduzione dei tempi di inattività e l'aumento della qualità del prodotto. Le aziende più interessate alla PdM sono quelle che si occupano di produzione manifatturiera, che è l'area di interesse in questa tesi, ma anche tutte quelle che fanno della efficienza degli impianti una loro prerogativa. Questa tesi nasce con l'obiettivo di applicare la PdM su un caso reale, proposto da una azienda del territorio che lavora in ambito manifatturiero, sfruttando le moderne tecnologie di Machine Learning e Analitica Previsionale. Nella tesi viene descritto approfonditamente il caso di studio in questione e la tecnologia che è stata utilizzata per portare a termine il progetto.
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Wang, Tianyi. "Trajectory Similarity Based Prediction for Remaining Useful Life Estimation." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282574910.

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Mishra, Madhav. "Model-based Prognostics for Prediction of Remaining Useful Life." Licentiate thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17263.

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Prognostics and healthmanagement (PHM) is an engineering discipline that aims to maintain the systembehaviour and function, and assure the mission success, safety andeffectiveness. Health management using a proper condition-based maintenance (CBM)deployment is a worldwide accepted technique and has grown very popular in manyindustries over the past decades. These techniques are relevant in environmentswhere the prediction of a failure and the prevention and mitigation of itsconsequences increase the profit and safety of the facilities concerned.Prognosis is the most critical part of this process and is nowadays recognizedas a key feature in maintenance strategies, since estimation of the remaininguseful life (RUL) is essential.PHM can provide a stateassessment of the future health of systems or components, e.g. when a degradedstate has been found. Using this technology, one can estimate how long it willtake before the equipment will reach a failure threshold, in future operatingconditions and future environmental conditions. This thesis focuses especiallyon physics-based prognostic approaches, which depend on a fundamentalunderstanding of the physical system in order to develop condition monitoringtechniques and to predict the RUL.The overall research objective of thework performed for this thesis has been to improve the accuracy and precisionof RUL predictions. The research hypothesis is that fusing the output of morethan one method will improve the accuracy and precision of the RUL estimation,by developing a new approach to prognostics that combines different remaininglife estimators and physics-based and data-driven methods. There are two waysof acquiring data for data-driven models, namely measurements of real systemsand syntactic data generation from simulations. The thesis deals with two casestudies, the first of which concerns the generation of synthetic data andindirect measurement of dynamic bearing loads and was performed atBillerudKorsäs paper mill at Karlsborg in Sweden. In this study the behaviourof a roller in a paper machine was analysed using the finite element method(FEM). The FEM model is a step towards the possibility of generating syntheticdata on different failure modes, and the possibility of estimating crucialparameters like dynamic bearing forces by combining real vibration measurementswith the FEM model. The second case study deals with the development ofprognostic methods for battery discharge estimation for Mars-based rovers. Herephysical models and measurement data were used in the prognostic development insuch a way that the degradation behaviour of the battery could be modelled andsimulated in order to predict the life-length. A particle filter turned out tobe the method of choice in performing the state assessment and predicting thefuture degradation. The method was then applied to a case study of batteriesthat provide power to the rover.
Godkänd; 2015; 20151116 (madmis); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Madhav Mishra Ämne: Drift och underhållsteknik/Operation and Maintenance Engineering Uppsats: Model-based Prognostics for Prediction of Remaining Useful Life Examinator: Professor Uday Kumar Institutionen för samhällsbyggnad och naturresurser Avdelning Drift, underhåll och akustik Luleå tekniska universitet Diskutant: Accos. Professor Jyoti Kumar Sinha University of Manchester, Aerospace and Civil Engineering, Manchester Tid: Torsdag 17 december 2015 kl 10.00 Plats: F1031, Luleå tekniska universitet
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Liu, Gang. "A Study on Remaining Useful Life Prediction for Prognostic Applications." ScholarWorks@UNO, 2011. http://scholarworks.uno.edu/td/456.

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We consider the prediction algorithm and performance evaluation for prognostics and health management (PHM) problems, especially the prediction of remaining useful life (RUL) for the milling machine cutter and lithium ‐ ion battery. We modeled battery as a voltage source and internal resisters. By analyzing voltage change trend during discharge, we made the prediction of battery remain discharge time in one discharge cycle. By analyzing internal resistance change trend during multiple cycles, we were able to predict the battery remaining useful time during its life time. We showed that the battery rest profile is correlated with the RUL. Numerical results using the realistic battery aging data from NASA prognostics data repository yielded satisfactory performance for battery prognosis as measured by certain performance metrics. We built a battery test platform and simulated more usage pattern and verified the prediction algorithm. Prognostic performance metrics were used to compare different algorithms.
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Bektas, Oguz. "An adaptive data filtering model for remaining useful life estimation." Thesis, University of Warwick, 2018. http://wrap.warwick.ac.uk/106052/.

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The field of Prognostics and Health Management is becoming ever more important in the modern maintenance era, with advanced techniques of automation and mechanisation becoming increasingly prevalent. Prognostic technology has promising abilities to forecast remaining useful life and likely future circumstances of complex systems. However, the evolution of data processing and its critical impact on remaining useful life predictions continually demand increasing development so as to meet higher performance levels. There is often a gap between the adequacy of prognostic pre-processing and the prediction methods. One way to reduce this gap would be to design an adaptive data processing method that can filter multidimensional condition monitoring data by incorporating useful information from multiple data sources. Due to the incomplete understanding on the multi-dimensional failure mechanisms and the collaboration between data sources, current prognostic methods lack the ability to deal effectively with complicated interdependency, multidimensional condition monitoring information and noisy data. Further conventional methods are unable to deal with these efficiently. The methodology proposed in this research handles these deficiencies by introducing a prognostic framework that allows the effective use of monitoring data from different resources to predict the lifetime of systems. The methodology presents a feed-forward neural network filtering approach for trajectory similarity based remaining useful life predictions. The extraction of health indicators is applied as a type of dynamic filtering, in which the time series having full operational conditions are used to train a neural network mapping between raw training inputs and a health indicator output. This trained network function is evaluated by repeating condition monitoring information from multiple data subsets. After the network filtering, the training trajectories are used as baselines to predict the future behaviours of test trajectories. The similarity between these data subsets compares the relationship between the historical performance deterioration of a system's prior operating period with a similar system's degradation behaviour. The proposed prognostic technique, together with dynamic data filtering and remaining useful estimation, holds the promise of increased prediction performance levels. The presented methodology was tested using the PHM08 data challenge provided by the Prognostics Centre of Excellence at NASA Ames Research Centre, and it achieved the overall leading score in the published literature.
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Mosallam, Ahmed. "Remaining useful life estimation of critical components based on Bayesian Approaches." Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2069/document.

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La construction de modèles de pronostic nécessite la compréhension du processus de dégradation des composants critiques surveillés afin d’estimer correctement leurs durées de fonctionnement avant défaillance. Un processus de d´dégradation peut être modélisé en utilisant des modèles de Connaissance issus des lois de la physique. Cependant, cette approche n´nécessite des compétences Pluridisciplinaires et des moyens expérimentaux importants pour la validation des modèles générés, ce qui n’est pas toujours facile à mettre en place en pratique. Une des alternatives consiste à apprendre le modèle de dégradation à partir de données issues de capteurs installés sur le système. On parle alors d’approche guidée par des données. Dans cette thèse, nous proposons une approche de pronostic guidée par des données. Elle vise à estimer à tout instant l’état de santé du composant physique et prédire sa durée de fonctionnement avant défaillance. Cette approche repose sur deux phases, une phase hors ligne et une phase en ligne. Dans la phase hors ligne, on cherche à sélectionner, parmi l’ensemble des signaux fournis par les capteurs, ceux qui contiennent le plus d’information sur la dégradation. Cela est réalisé en utilisant un algorithme de sélection non supervisé développé dans la thèse. Ensuite, les signaux sélectionnés sont utilisés pour construire différents indicateurs de santé représentant les différents historiques de données (un historique par composant). Dans la phase en ligne, l’approche développée permet d’estimer l’état de santé du composant test en faisant appel au filtre Bayésien discret. Elle permet également de calculer la durée de fonctionnement avant défaillance du composant en utilisant le classifieur k-plus proches voisins (k-NN) et le processus de Gauss pour la régression. La durée de fonctionnement avant défaillance est alors obtenue en comparant l’indicateur de santé courant aux indicateurs de santé appris hors ligne. L’approche développée à été vérifiée sur des données expérimentales issues de la plateforme PRO-NOSTIA sur les roulements ainsi que sur des données fournies par le Prognostic Center of Excellence de la NASA sur les batteries et les turboréacteurs
Constructing prognostics models rely upon understanding the degradation process of the monitoredcritical components to correctly estimate the remaining useful life (RUL). Traditionally, a degradationprocess is represented in the form of physical or experts models. Such models require extensiveexperimentation and verification that are not always feasible in practice. Another approach that buildsup knowledge about the system degradation over time from component sensor data is known as datadriven. Data driven models require that sufficient historical data have been collected.In this work, a two phases data driven method for RUL prediction is presented. In the offline phase, theproposed method builds on finding variables that contain information about the degradation behaviorusing unsupervised variable selection method. Different health indicators (HI) are constructed fromthe selected variables, which represent the degradation as a function of time, and saved in the offlinedatabase as reference models. In the online phase, the method estimates the degradation state usingdiscrete Bayesian filter. The method finally finds the most similar offline health indicator, to the onlineone, using k-nearest neighbors (k-NN) classifier and Gaussian process regression (GPR) to use it asa RUL estimator. The method is verified using PRONOSTIA bearing as well as battery and turbofanengine degradation data acquired from NASA data repository. The results show the effectiveness ofthe method in predicting the RUL
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Siegel, David. "Evaluation of Health Assessment Techniques for Rotating Machinery." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1250282528.

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Wengbrand, Frida, and Sofia Eriksson. "Remaining useful life of customer relationships : Valuation in accordance with IFRS 3." Thesis, Jönköping University, Jönköping International Business School, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-500.

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In the year of 2000 the European Commission adopted a communication

called EU Financial Reporting Strategy: the Way Forward. The communication intended to make all listed companies within the EU arrange their financial statements in accordance with

International Accounting Standards by 2005 at the latest. When the amendments of IFRS 3 was introduced in March 2004 it meant that companies from that moment on, when acquiring another company, have to allocate the part of the purchase price assignable to customer contracts and the related customer relationships as an intangible asset. IFRS 3 does not give any guidance whatsoever on how to

accomplish the above described allocation and estimate a true and fair value of customer contracts and relationships. Let alone any direction regarding the establishment of the remaining useful life of the customer relationships and contracts, which constitutes the

foundation of the fair valuation but also a true and fair view regarding amortizations. The purpose of this thesis is to examine how the establishments regarding remaining useful life of customer relationships and contracts have been done. Furthermore, the purpose of this thesis is to explain the decision process and motives that results in why management choose to apply the specific remaining useful life of customer relationships and contracts they do. This study has been carried out with a qualitative approach involving two listed group companies within three different industries, hence, six companies are involved in this thesis. Semi-structured telephone interviews have been made with the companies and the annual reports have been examined. In order to explain the actions behind the valuation and establishment process, the positive accounting theory has been used. None of the six companies taking part in this study have applied an outspoken method for the establishment of the remaining useful life of the customer relationships and contracts and only half of the companies have identified different customer groups. A relation can be identified between using an external consultant

and applying different remaining lives for different customer groups. All companies amortize the customer relationships and contracts on a straight-line basis. This can be explained by the positive accounting theory to some extent. All companies applied straight-line amortization even though some of them actually admit that a declining

balance would provide a fairer view. Furthermore, long amortization plans are used in some companies in order to decrease the amortization costs and hence increase the net income. Positive accounting has been applied in order to shift reported earnings.


Under år 2000 beslutade den Europeiska kommissionen om att anta ett

förslag som hette EU Financial Reporting Strategy: the Way Forward. Antagandet av förslaget innebar att alla noterade bolag inom EU skulle presentera sin redovisning och sina årsredovisningar i linje med bestämmelserna i IAS – International Accounting Standards senast år 2005. När lagändringarna i IFRS 3 introducerades i mars 2004 innebar det att noterade bolag vid företagsförvärv fortsättningsvis skulle allokera den del av köpeskillingen som är hänförlig till kundkontrakt och relaterade kundrelationer som immateriell tillgång i

balansräkningen. IFRS 3 ger ingen vägledning överhuvudtaget med avseende på hur bolagen ska genomföra den ovan beskrivna allokeringen och uppskatta ett rättvist värde på kundkontrakt och kundrelationer. Inte heller finns det någon anvisning angående fastställandet av livslängd på kundkontrakt och kundrelationer som i sin tur ligger till grund för en rättvis värdering och en rättvis avskrivningsplan.

Syftet med den här uppsatsen är att undersöka hur fastställandet av livslängden på kundrelationer och kundkontrakt har utförts. Syftet är även att förklara beslutsprocessen och de bakomliggande motiven till varför företagsledningen väljer att använda den livslängd på kundrelationer och kundkontrakt de faktiskt gör. Studien har genomförts med en kvalitativ ansats som har involverat två noterade

koncernbolag inom tre olika branscher, totalt har alltså sex bolag medverkat i uppsatsen. Semistrukturerade telefonintervjuer har gjorts med de involverade bolagen och även deras årsredovisningar har undersökts. För att kunna förklara handlandet angående värderingsprocessen och livslängdsprocessen har den positiva redovisningsteorin använts. Inget av de sex bolagen som medverkat i studien har använt sig av någon etablerad metod för att fastställa den återstående livslängden av kundrelationerna och kundkontrakten, och endast hälften av företagen har identifierat olika grupper av kunder. Ett samband har identifierats mellan att använda sig av en extern konsult vid fastställandet och att använda sig av olika återstående livslängder för olika kundgrupper. Alla sex företagen använder sig av linjär avskrivning på kundkontrakten och kundrelationerna. Detta kan till en viss gräns förklaras med positiv redovisningsteori. Alla företagen har använt sig av linjär avskrivning även om vissa av företagen till och med medger att

degressiv avskrivning skulle ge en mer rättvis bild. Dessutom har långa avskrivningstider använts i en del av företagen för att sänka avskrivningskostnaderna som i sin tur ökar resultatet. Positiv redovisningsteori har alltså använts för att flytta vinster till innevarande år.

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Liang, Jie Jun Yi. "Novel framework for wind turbine fault diagnosis and remaining useful life prediction." Thesis, University of Macau, 2015. http://umaclib3.umac.mo/record=b3335776.

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Oyharcabal, Astorga Nicolás. "Convolutional recurrent neural networks for remaining useful life prediction in mechanical systems." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168514.

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Memoria para optar al título de Ingeniero Civil Mecánico
La determinación de la vida útil remanente (RUL del inglés "Remaining Useful Life") de una máquina, equipo, dispositivo o elemento mecánico, es algo en lo que se ha estado trabajando en los últimos años y que es crucial para el futuro de cualquier industria que así lo requiera. El continuo monitoreo de máquinas junto a una buena predicción de la RUL permite la minimización de costos de mantención y menor exposición a fallas. Sin embargo, los datos obtenidos del monitoreo son variados, tienen ruido, poseen un carácter secuencial y no siempre guardan estricta relación con la RUL, por lo que su estimación es un problema difícil. Es por ello que en la actualidad se utilizan distintas clases de Redes Neuronales y en particular, cuando se quiere modelar problemas de carácter secuencial, se utilizan las Redes Neuronales Recurrentes o RNN (del inglés "Recurrent Neural Network") como LSTM (del inglés "Long Short Term Memory") o JANET (del inglés "Just Another NETwork"), por su capacidad para identificar de forma autónoma patrones en secuencias temporales, pero también junto a estas últimas redes, también se utilizan alternativas que incorporan la Convolución como operación para cada célula de las RNN y que se conocen como ConvRNN (del inglés "Convolutional Recurrent Neural Network"). Estas últimas redes son mejores que sus pares convolucional y recurrentes en ciertos casos que requieren procesar secuencias de imágenes, y en el caso particular de este trabajo, series de tiempo de datos de monitoreo que son suavizados por la Convolución y procesados por la Recurrencia. El objetivo general de este trabajo es determinar la mejor opción de ConvRNN para la determinación de la RUL de un turbofan a partir de series de tiempo de la base de datos C-MAPSS. También se estudia cómo editar la base de datos para mejorar la precisión de una ConvRNN y la aplicación de la Convolución como una operación primaria en una serie de tiempo cuyos parámetros muestran el comportamiento de un turbofan. Para ello se programa una LSTM Convolucional, LSTM Convolucional Codificador-Decodificador, JANET Convolucional y JANET Convolucional Codificador-Decodificador. A partir de esto se encuentra que el modelo JANET Convolucional Codificador-Decodificador da los mejores resultados en cuanto a exactitud promedio y cantidad de parámetros necesarios (entre menos mejor pues se necesita menos memoria) para la red, siendo además capaz de asimilar la totalidad de las bases de datos C-MAPSS. Por otro lado, también se encuentra que la RUL de la base de datos puede ser modificada para datos antes de la falla. Para la programación y puesta en marcha de las diferentes redes, se utilizan los computadores del laboratorio de Integración de Confiabilidad y Mantenimiento Inteligente (ICMI) del Departamento de Ingeniería Mecánica de la Universidad de Chile.
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Books on the topic "Remainiing useful Life"

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Si, Xiao-Sheng, Zheng-Xin Zhang, and Chang-Hua Hu. Data-Driven Remaining Useful Life Prognosis Techniques. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54030-5.

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Lei, Yaguo. Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery. Elsevier Science & Technology Books, 2016.

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Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery. Elsevier, 2017. http://dx.doi.org/10.1016/c2016-0-00367-4.

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Si, Xiao-Sheng. Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications. Springer, 2018.

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Si, Xiao-Sheng, Zheng-Xin Zhang, and Chang-Hua Hu. Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications. Springer, 2017.

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Kucharski, Fred, and Muhammad Adnan Abid. Interannual Variability of the Indian Monsoon and Its Link to ENSO. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.615.

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The interannual variability of Indian summer monsoon is probably one of the most intensively studied phenomena in the research area of climate variability. This is because even relatively small variations of about 10% to 20% from the mean rainfall may have dramatic consequences for regional agricultural production. Forecasting such variations months in advance could help agricultural planning substantially. Unfortunately, a perfect forecast of Indian monsoon variations, like any other regional climate variations, is impossible in a long-term prediction (that is, more than 2 weeks or so in advance). The reason is that part of the atmospheric variations influencing the monsoon have an inherent predictability limit of about 2 weeks. Therefore, such predictions will always be probabilistic, and only likelihoods of droughts, excessive rains, or normal conditions may be provided. However, even such probabilistic information may still be useful for agricultural planning. In research regarding interannual Indian monsoon rainfall variations, the main focus is therefore to identify the remaining predictable component and to estimate what fraction of the total variation this component accounts for. It turns out that slowly varying (with respect to atmospheric intrinsic variability) sea-surface temperatures (SSTs) provide the dominant part of the predictable component of Indian monsoon variability. Of the predictable part arising from SSTs, it is the El Niño Southern Oscillation (ENSO) that provides the main part. This is not to say that other forcings may be neglected. Other forcings that have been identified are, for example, SST patterns in the Indian Ocean, Atlantic Ocean, and parts of the Pacific Ocean different from the traditional ENSO region, and springtime snow depth in the Himalayas, as well as aerosols. These other forcings may interact constructively or destructively with the ENSO impact and thus enhance or reduce the ENSO-induced predictable signal. This may result in decade-long changes in the connection between ENSO and the Indian monsoon. The physical mechanism for the connection between ENSO and the Indian monsoon may be understood as large-scale adjustment of atmospheric heatings and circulations to the ENSO-induced SST variations. These adjustments modify the Walker circulation and connect the rising/sinking motion in the central-eastern Pacific during a warm/cold ENSO event with sinking/rising motion in the Indian region, leading to reduced/increased rainfall.
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Book chapters on the topic "Remainiing useful Life"

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Zhang, Hong-chao, Shujie Liu, Huitian Lu, Yuanliang Zhang, and Yawei Hu. "Remanufacturing and Remaining Useful Life Assessment." In Handbook of Manufacturing Engineering and Technology, 3137–93. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-4670-4_112.

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Zhang, Hongchao, Shujie Liu, Huitian Lu, and Yuanliang Zhang. "Remanufacturing and Remaining Useful Life Assessment." In Handbook of Manufacturing Engineering and Technology, 1–49. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-4976-7_112-1.

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Rahimdel, Mohammad Javad, Behzad Ghodrati, and Amir Taghizadeh Vahed. "Prediction of Mining Railcar Remaining Useful Life." In Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019, 281–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33954-8_35.

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Wang, W., and M. J. Carr. "Component Level Replacements: Estimating Remaining Useful Life." In Complex Engineering Service Systems, 297–314. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-189-9_16.

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Mrugalska, Beata. "Remaining Useful Life as Prognostic Approach: A Review." In Human Systems Engineering and Design, 689–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02053-8_105.

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Soons, Youri, Remco Dijkman, Maurice Jilderda, and Wouter Duivesteijn. "Predicting Remaining Useful Life with Similarity-Based Priors." In Lecture Notes in Computer Science, 483–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44584-3_38.

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Xiong, Rui. "Remaining Useful Life Prediction of Lithium-Ion Batteries." In Battery Management Algorithm for Electric Vehicles, 217–42. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0248-4_6.

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Harpale, Abhay. "Chronologically Guided Deep Network for Remaining Useful Life Estimation." In Machine Learning, Optimization, and Data Science, 118–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64580-9_10.

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Vasan, Arvind Sai Sarathi, and Michael G. Pecht. "Health and Remaining Useful Life Estimation of Electronic Circuits." In Prognostics and Health Management of Electronics, 279–327. Chichester, UK: John Wiley and Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119515326.ch11.

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Boškoski, Pavle, Bojan Musizza, Boštjan Dolenc, and Ðani Juričić. "Entropy Indices for Estimation of the Remaining Useful Life." In Applied Condition Monitoring, 373–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62042-8_34.

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Conference papers on the topic "Remainiing useful Life"

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Egan, G. A., and B. V. Andrews. "Calculating Remaining Useful Life of Ships." In Offshore Technology Conference. Offshore Technology Conference, 1992. http://dx.doi.org/10.4043/6975-ms.

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Malinowski, Simon, Brigitte Chebel-Morello, and Noureddine Zerhouni. "Shapelet-based remaining useful life estimation." In 2014 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2014. http://dx.doi.org/10.1109/coase.2014.6899416.

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Bagul, Yogesh G., Ibrahim Zeid, and Sagar V. Kamarthi. "Overview of Remaining Useful Life Methodologies." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49938.

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Nowadays, it is imperative for products to function properly each time they are used. If a product fails during its use, it may have disastrous consequences. Estimating remaining useful life (RUL) of a product is a key to prevent such disasters, improve its reliability, provide security and reduce maintenance and operational cost. Naturally, estimation of RUL of a product and develop methodologies for the same is proving an interesting domain for researchers. This paper gives an overview of RUL estimation methodologies applied to various products. It also discusses hybrid methodologies which improve RUL estimation accuracy and overcome limitations of the individual methodologies. As this is an emerging area, these methodologies have been applied to only a handful of products. A list of these products is provided with references. A quantitative metric that measures the relative important characteristic differences among different methodologies is given. This paper concludes with few important points observed during literature review.
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Stephen Ekwaro-Osire, Haileyesus B. Endeshaw, Duc H. Pham, and Fisseha M. Alemayehu. "Uncertainty in Remaining Useful Life Prediction." In 23rd ABCM International Congress of Mechanical Engineering. Rio de Janeiro, Brazil: ABCM Brazilian Society of Mechanical Sciences and Engineering, 2015. http://dx.doi.org/10.20906/cps/cob-2015-2705.

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Ahmadzadeh, Farzaneh, Jonas Biteus, and Olof Steinert. "Remaining useful life Prediction of air spring." In 2019 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2019. http://dx.doi.org/10.1109/icphm.2019.8819413.

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Rozhkovskaya, Alena A., and Igor O. Sychev. "Problems of Predicting the Remaining Useful Life." In 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2020. http://dx.doi.org/10.1109/eiconrus49466.2020.9039369.

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RAJARAM, SATISH, UTKU GUCLU, PRASHANTH ABRAHAM, SHANE ESOLA, and ANTONIOS KONTSOS. "Remaining Useful Life Estimations Using Acoustic Emissions." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/300.

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Zhou, Jianbao, Datong Liu, Yu Peng, and Xiyuan Peng. "Dynamic battery remaining useful life estimation: An on-line data-driven approach." In 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2012. http://dx.doi.org/10.1109/i2mtc.2012.6229280.

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Dickerson, Andrew, Ravi Rajamani, Mike Boost, and John Jackson. "Determining Remaining Useful Life for Li-ion Batteries." In SAE 2015 AeroTech Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2015. http://dx.doi.org/10.4271/2015-01-2584.

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Mosallam, A., K. Medjaher, and N. Zerhouni. "Integrated Bayesian framework for remaining useful life prediction." In 2014 IEEE Conference on Prognostics and Health Management (PHM). IEEE, 2014. http://dx.doi.org/10.1109/icphm.2014.7036361.

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Reports on the topic "Remainiing useful Life"

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Simmons, Kevin L., Leonard S. Fifield, Matthew P. Westman, Pradeep Ramuhalli, Allan F. Pardini, Jonathan R. Tedeschi, and Anthony M. Jones. Determining Remaining Useful Life of Aging Cables in Nuclear Power Plants ? Interim Study FY13. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1095453.

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Lissenden, Cliff, Tasnin Hassan, and Vijaya Rangari. Monitoring microstructural evolution of alloy 617 with non-linear acoustics for remaining useful life prediction; multiaxial creep-fatigue and creep-ratcheting. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1214660.

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Simmons, Kevin L., Pradeep Ramuhalli, David L. Brenchley, Jamie B. Coble, Hash Hashemian, Robert Konnik, and Sheila Ray. Light Water Reactor Sustainability (LWRS) Program ? Non-Destructive Evaluation (NDE) R&D Roadmap for Determining Remaining Useful Life of Aging Cables in Nuclear Power Plants. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1097978.

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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.

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Abstract:
Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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