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Academic literature on the topic 'Remaining useful life estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Remaining useful life estimation"

1

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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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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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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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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Lyu, Jianhua, Rongrong Ying, Ningyun Lu, and Baili Zhang. "Remaining useful life estimation with multiple local similarities." Engineering Applications of Artificial Intelligence 95 (October 2020): 103849. http://dx.doi.org/10.1016/j.engappai.2020.103849.

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Nguyen, Thi-Bich-Lien, Mohand Djeziri, Bouchra Ananou, Mustapha Ouladsine, and Jacques Pinaton. "Remaining Useful Life estimation for noisy degradation trends." IFAC-PapersOnLine 48, no. 21 (2015): 85–90. http://dx.doi.org/10.1016/j.ifacol.2015.09.509.

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Jiang, Zengqiang, Dragan Banjevic, Mingcheng E., Andrew Jardine, and Qi Li. "Remaining useful life estimation of metropolitan train wheels considering measurement error." Journal of Quality in Maintenance Engineering 24, no. 4 (October 8, 2018): 422–36. http://dx.doi.org/10.1108/jqme-04-2016-0017.

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Purpose The purpose of this paper is to develop an approach for estimating the remaining useful life (RUL) of metropolitan train wheels considering measurement error. Design/methodology/approach The paper proposes a wear model of a metropolitan train wheel based on a discrete state space model; the model considers the wheel’s stochastic degradation and measurement error simultaneously. The paper estimates the RUL on the basis of the estimated degradation state. Finally, it presents a case study to verify the proposed approach. The results indicate that the proposed method is superior to methods that do not consider measurement error and can improve the accuracy of the estimated RUL. Findings RUL estimation is a key issue in condition-based maintenance and prognostics and health management. With the rapid development of advanced sensor technologies and data acquisition facilities for the maintenance of metropolitan train wheels, condition monitoring (CM) is becoming more accurate and more affordable, creating the possibility of estimating the RUL of wheels using CM data. However, the measurements of the wheels, especially the wayside measurements, are not yet precise enough. On the other hand, few existing studies of the RUL estimation of train wheels consider measurement error. Practical implications The approach described in this paper will make the RUL estimation of metropolitan train wheels easier and more precise. Originality/value Hundreds of million yuan are wasted every year due to over re-profiling of rail wheels in China. The ability to precisely estimate RUL will reduce the number of re-profiling activities and achieve significant economic benefits. More generally, the paper could enrich the body of knowledge of RUL estimation for a slowly degrading system considering measurement error.

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Malinowski, Simon, Brigitte Chebel-Morello, and Noureddine Zerhouni. "Remaining useful life estimation based on discriminating shapelet extraction." Reliability Engineering & System Safety 142 (October 2015): 279–88. http://dx.doi.org/10.1016/j.ress.2015.05.012.

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Wang, Hai-Kun, Yan-Feng Li, Yu Liu, Yuan-Jian Yang, and Hong-Zhong Huang. "Remaining useful life estimation under degradation and shock damage." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 229, no. 3 (March 10, 2015): 200–208. http://dx.doi.org/10.1177/1748006x15573046.

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Nguyen, Hoa Dinh. "A data-driven framework for remaining useful life estimation." Vietnam Journal of Science and Technology 55, no. 5 (October 20, 2017): 557. http://dx.doi.org/10.15625/2525-2518/55/5/8582.

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Remaining useful life (RUL) estimation is one of the most common tasks in the field of prognostics and structural health management. The aim of this research is to estimate the remaining useful life of an unspecified complex system using some data-driven approaches. The approaches are suitable for problems in which a data library of complete runs of a system is available. Given a non-complete run of the system, the RUL can be predicted using these approaches. Three main RUL prediction algorithms, which cover centralized data processing, decentralize data processing, and in-between, are introduced and evaluated using the data of PHM’08 Challenge Problem. The methods involve the use of some other data processing techniques including wavelets denoise and similarity search. Experiment results show that all of the approaches are effective in performing RUL prediction.

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Dissertations / Theses on the topic "Remaining useful life estimation"

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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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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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Tamssaouet, Ferhat. "Towards system-level prognostics : modeling, uncertainty propagation and system remaining useful life prediction." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0079.

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Le pronostic est le processus de prédiction de la durée de vie résiduelle utile (RUL) des composants, sous-systèmes ou systèmes. Cependant, jusqu'à présent, le pronostic a souvent été abordé au niveau composant sans tenir compte des interactions entre les composants et l'impact de l'environnement, ce qui peut conduire à une mauvaise prédiction du temps de défaillance dans des systèmes complexes. Dans ce travail, une approche de pronostic au niveau du système est proposée. Cette approche est basée sur un nouveau cadre de modélisation : le modèle d'inopérabilité entrée-sortie (IIM), qui permet de prendre en compte les interactions entre les composants et les effets du profil de mission et peut être appliqué pour des systèmes hétérogènes. Ensuite, une nouvelle méthodologie en ligne pour l'estimation des paramètres (basée sur l'algorithme de la descente du gradient) et la prédiction du RUL au niveau système (SRUL) en utilisant les filtres particulaires (PF), a été proposée. En détail, l'état de santé des composants du système est estimé et prédit d'une manière probabiliste en utilisant les PF. En cas de divergence consécutive entre les estimations a priori et a posteriori de l'état de santé du système, la méthode d'estimation proposée est utilisée pour corriger et adapter les paramètres de l'IIM. Finalement, la méthodologie développée, a été appliquée sur un système industriel réaliste : le Tennessee Eastman Process, et a permis une prédiction du SRUL dans un temps de calcul raisonnable
Prognostics is the process of predicting the remaining useful life (RUL) of components, subsystems, or systems. However, until now, the prognostics has often been approached from a component view without considering interactions between components and effects of the environment, leading to a misprediction of the complex systems failure time. In this work, a prognostics approach to system-level is proposed. This approach is based on a new modeling framework: the inoperability input-output model (IIM), which allows tackling the issue related to the interactions between components and the mission profile effects and can be applied for heterogeneous systems. Then, a new methodology for online joint system RUL (SRUL) prediction and model parameter estimation is developed based on particle filtering (PF) and gradient descent (GD). In detail, the state of health of system components is estimated and predicted in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, the proposed estimation method is used to correct and to adapt the IIM parameters. Finally, the developed methodology is verified on a realistic industrial system: The Tennessee Eastman Process. The obtained results highlighted its effectiveness in predicting the SRUL in reasonable computing time

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Mat, Jihin Rosmawati [Verfasser], and Dirk [Akademischer Betreuer] Söffker. "Structural Health Assessment and Remaining Useful Life Estimation for Industrial System / Rosmawati Mat Jihin ; Betreuer: Dirk Söffker." Duisburg, 2019. http://d-nb.info/119811150X/34.

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Yang, Lei. "Methodology of Prognostics Evaluation for Multiprocess Manufacturing Systems." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298043095.

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Maré, Charl Francois. "An investigation of CFD simulation for estimation of turbine RUL." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/69152.

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Turbines encounter blade failures due to fatigue and creep. It has been shown in the literature that the primary cause of steam turbine blade failures worldwide can be ascribed to fatigue in low pressure (LP) turbine blades. The failure and damage to these blades can lead to catastrophic consequences. Some utilities use empirical methods to determine the forces experienced by turbine blades but desire more accurate methods. The inaccurate prediction of high-cycle fatigue (HCF), thermal durability and stage performance is introduced when one does not consider blade row interaction. Blade row interactions can, however, be accounted for by means of computational fluid dynamics (CFD). Furthermore, modern high- fidelity CFD tools would be able to contribute greatly in predicting the forces experienced by turbine blades. Numerical tools such as CFD and nite element analysis (FEA) can greatly contribute to the estimation of the remaining useful life (RUL) of turbine blades. However, in this estimation process, there are various uncertainties and aspects that affect the estimated RUL. Understanding the sensitivity of the estimated RUL to these various uncertainties and aspects is of great importance if RUL is to be estimated as accurately as possible. In this dissertation, a sensitivity analysis is performed with the purpose of establishing the sensitivity of the estimated RUL of the last stage rotor of an LP steam turbine, to the number of harmonics used in a nonlinear harmonic (NLH) CFD simulation. The sensitivity of the estimated RUL is evaluated in the HCF regime, where the cyclic stresses occur below the yield strength of the turbine blade. A CFD model, FE model, and fatigue model were therefore developed in such a manner that would suffice, regarding the purpose of the sensitivity analysis. The CFD model is validated by comparing the predicted CFD power to that of actual generated power of a dual 100MW LP steam turbine. The sensitivity analysis is performed for 3 operation conditions, and for each operational condition the aerodynamic forces were computed using 1, 2, and 3 harmonics in an NLH simulation. The estimation process considers a weak coupling between the CFD model and FE model. NLH simulations are firstly performed to calculate the unsteady static surface pressure distributions on the last stage rotor. This is followed by the mapping thereof to the FE model, for which a transient structural analysis is performed. Finally, the RUL is estimated by performing a fatigue analysis on the stress history obtained from the transient structural analysis. Based on the results of the sensitivity analysis, the following recommendations were made, from a conservative point of view. Firstly, in general, if the RUL is to be estimated with reasonable accuracy, just using 1 harmonic in an NLH simulation will not be sufficient and 2 harmonics should be used. Secondly, if the RUL has to be estimated with high accuracy, 3 harmonics should be used.
Dissertation (MEng)--University of Pretoria, 2018.
National Research Foundation (NRF)
Mechanical and Aeronautical Engineering
MEng
Unrestricted

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Spataru, Mihai. "Battery aging diagnosis and prognosis for Hybrid Electrical Vehicles Applications." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366364019.

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Le, Thanh Trung. "Contribution to deterioration modeling and residual life estimation based on condition monitoring data." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT099/document.

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La maintenance prédictive joue un rôle important dans le maintien des systèmes de production continue car elle peut aider à réduire les interventions inutiles ainsi qu'à éviter des pannes imprévues. En effet, par rapport à la maintenance conditionnelle, la maintenance prédictive met en œuvre une étape supplémentaire, appelée le pronostic. Les opérations de maintenance sont planifiées sur la base de la prédiction des états de détérioration futurs et sur l'estimation de la vie résiduelle du système. Dans le cadre du projet européen FP7 SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment en Anglais), cette thèse se concentre sur le développement des modèles de détérioration stochastiques et sur des méthodes d'estimation de la vie résiduelle (Remaining Useful Life – RUL en anglais) associées pour les adapter aux cas d'application du projet. Plus précisément, les travaux présentés dans ce manuscrit sont divisés en deux parties principales. La première donne une étude détaillée des modèles de détérioration et des méthodes d'estimation de la RUL existant dans la littérature. En analysant leurs avantages et leurs inconvénients, une adaptation d’une approche de l'état de l'art est mise en œuvre sur des cas d'études issus du projet SUPREME et avec les données acquises à partir d’un banc d'essai développé pour le projet. Certains aspects pratiques de l’implémentation, à savoir la question de l'échange d'informations entre les partenaires du projet, sont également détaillées dans cette première partie. La deuxième partie est consacrée au développement de nouveaux modèles de détérioration et les méthodes d'estimation de la RUL qui permettent d'apporter des éléments de solutions aux problèmes de modélisation de détérioration et de prédiction de RUL soulevés dans le projet SUPREME. Plus précisément, pour surmonter le problème de la coexistence de plusieurs modes de détérioration, le concept des modèles « multi-branche » est proposé. Dans le cadre de cette thèse, deux catégories des modèles de type multi-branche sont présentées correspondant aux deux grands types de modélisation de l'état de santé des système, discret ou continu. Dans le cas discret, en se basant sur des modèles markoviens, deux modèles nommés Mb-HMM and Mb-HsMM (Multi-branch Hidden (semi-)Markov Model en anglais) sont présentés. Alors que dans le cas des états continus, les systèmes linéaires à sauts markoviens (JMLS) sont mis en œuvre. Pour chaque modèle, un cadre à deux phases est implémenté pour accomplir à la fois les tâches de diagnostic et de pronostic. A travers des simulations numériques, nous montrons que les modèles de type multi-branche peuvent donner des meilleures performances pour l'estimation de la RUL par rapport à celles obtenues par des modèles standards mais « mono-branche »
Predictive maintenance plays a crucial role in maintaining continuous production systems since it can help to reduce unnecessary intervention actions and avoid unplanned breakdowns. Indeed, compared to the widely used condition-based maintenance (CBM), the predictive maintenance implements an additional prognostics stage. The maintenance actions are then planned based on the prediction of future deterioration states and residual life of the system. In the framework of the European FP7 project SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment), this thesis concentrates on the development of stochastic deterioration models and the associated remaining useful life (RUL) estimation methods in order to be adapted in the project application cases. Specifically, the thesis research work is divided in two main parts. The first one gives a comprehensive review of the deterioration models and RUL estimation methods existing in the literature. By analyzing their advantages and disadvantages, an adaption of the state of the art approaches is then implemented for the problem considered in the SUPREME project and for the data acquired from a project's test bench. Some practical implementation aspects, such as the issue of delivering the proper RUL information to the maintenance decision module are also detailed in this part. The second part is dedicated to the development of innovative contributions beyond the state-of-the-are in order to develop enhanced deterioration models and RUL estimation methods to solve original prognostics issues raised in the SUPREME project. Specifically, to overcome the co-existence problem of several deterioration modes, the concept of the "multi-branch" models is introduced. It refers to the deterioration models consisting of different branches in which each one represent a deterioration mode. In the framework of this thesis, two multi-branch model types are presented corresponding to the discrete and continuous cases of the systems' health state. In the discrete case, the so-called Multi-branch Hidden Markov Model (Mb-HMM) and the Multi-branch Hidden semi-Markov model (Mb-HsMM) are constructed based on the Markov and semi-Markov models. Concerning the continuous health state case, the Jump Markov Linear System (JMLS) is implemented. For each model, a two-phase framework is carried out for both the diagnostics and prognostics purposes. Through numerical simulations and a case study, we show that the multi-branch models can help to take into account the co-existence problem of multiple deterioration modes, and hence give better performances in RUL estimation compared to the ones obtained by standard "single branch" models

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Diallo, Ousmane Nasr. "A data analytics approach to gas turbine prognostics and health management." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/42845.

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As a consequence of the recent deregulation in the electrical power production industry, there has been a shift in the traditional ownership of power plants and the way they are operated. To hedge their business risks, the many new private entrepreneurs enter into long-term service agreement (LTSA) with third parties for their operation and maintenance activities. As the major LTSA providers, original equipment manufacturers have invested huge amounts of money to develop preventive maintenance strategies to minimize the occurrence of costly unplanned outages resulting from failures of the equipments covered under LTSA contracts. As a matter of fact, a recent study by the Electric Power Research Institute estimates the cost benefit of preventing a failure of a General Electric 7FA or 9FA technology compressor at $10 to $20 million. Therefore, in this dissertation, a two-phase data analytics approach is proposed to use the existing monitoring gas path and vibration sensors data to first develop a proactive strategy that systematically detects and validates catastrophic failure precursors so as to avoid the failure; and secondly to estimate the residual time to failure of the unhealthy items. For the first part of this work, the time-frequency technique of the wavelet packet transforms is used to de-noise the noisy sensor data. Next, the time-series signal of each sensor is decomposed to perform a multi-resolution analysis to extract its features. After that, the probabilistic principal component analysis is applied as a data fusion technique to reduce the number of the potentially correlated multi-sensors measurement into a few uncorrelated principal components. The last step of the failure precursor detection methodology, the anomaly detection decision, is in itself a multi-stage process. The obtained principal components from the data fusion step are first combined into a one-dimensional reconstructed signal representing the overall health assessment of the monitored systems. Then, two damage indicators of the reconstructed signal are defined and monitored for defect using a statistical process control approach. Finally, the Bayesian evaluation method for hypothesis testing is applied to a computed threshold to test for deviations from the healthy band. To model the residual time to failure, the anomaly severity index and the anomaly duration index are defined as defects characteristics. Two modeling techniques are investigated for the prognostication of the survival time after an anomaly is detected: the deterministic regression approach, and parametric approximation of the non-parametric Kaplan-Meier plot estimator. It is established that the deterministic regression provides poor prediction estimation. The non parametric survival data analysis technique of the Kaplan-Meier estimator provides the empirical survivor function of the data set comprised of both non-censored and right censored data. Though powerful because no a-priori predefined lifetime distribution is made, the Kaplan-Meier result lacks the flexibility to be transplanted to other units of a given fleet. The parametric analysis of survival data is performed with two popular failure analysis distributions: the exponential distribution and the Weibull distribution. The conclusion from the parametric analysis of the Kaplan-Meier plot is that the larger the data set, the more accurate is the prognostication ability of the residual time to failure model.

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Books on the topic "Remaining useful life estimation"

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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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Kumar, Bhishm. Estimation of rates and pattern of sedimentation and useful life of Dal-Nagin lake in J & K using natural fallout of Cs-137 & Pb-210 radioisotapes. Roorkee: National Institute of Hydrology, 2000.

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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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Cortes, Edgar. Useful Life Health Estimation for Valve - Regulated Lead Acid Batteries. Independently Published, 2020.

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Bhopal, Raj S. Summarizing, presenting, and interpreting epidemiological data: Building on incidence and prevalence. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198739685.003.0008.

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Basic epidemiological data on disease occurrence and population structure can be manipulated and presented in many ways. Epidemiological summary measures, broadly, estimate absolute or relative frequency of outcomes. Usually, relative measures are more useful in causal enquiry while absolute measures are better in health planning and policy. These measures, usually in association with risk factor prevalence data, allow estimation of the risk attributable to a risk factor in those exposed and in the entire population. Avoidable mortality (and morbidity) refers to the potential to avoid death (or morbidity) from a number of specified causes if the best possible health care actions were taken. Years of life saved measures help to measure the impact of avoidable mortality in the population. Epidemiological data on diseases can be combined with other information, such as socio-economic circumstances, social values and attitudes, and behaviours relevant to health, to build up a community health profile.

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Hector, Andy. The New Statistics with R. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198798170.001.0001.

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Statistics is a fundamental component of the scientific toolbox, but learning the basics of this area of mathematics is one of the most challenging parts of a research training. This book gives an up-to-date introduction to the classical techniques and modern extensions of linear-model analysis—one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. The book emphasizes an estimation-based approach that takes account of recent criticisms of overuse of probability values and introduces the alternative approach using information criteria. The book is based on the use of the open-source R programming language for statistics and graphics, which is rapidly becoming the lingua franca in many areas of science. This second edition adds new chapters, including one discussing some of the complexities of linear-model analysis and another introducing reproducible research documents using the R Markdown package. Statistics is introduced through worked analyses performed in R using interesting data sets from ecology, evolutionary biology, and environmental science. The data sets and R scripts are available as supporting material.

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Book chapters on the topic "Remaining useful life estimation"

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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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Costa, Nahuel, and Luciano Sánchez. "Remaining Useful Life Estimation Using a Recurrent Variational Autoencoder." In Lecture Notes in Computer Science, 53–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86271-8_5.

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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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Farhat, Mohamed Habib, Fakher Chaari, Xavier Chiementin, Fabrice Bolaers, and Mohamed Haddar. "Dynamic Remaining Useful Life Estimation for a Shaft Bearings System." In Applied Condition Monitoring, 169–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79519-1_11.

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Chai, Fang-Chien, Chun-Chih Lo, Mong-Fong Horng, and Yau-Hwang Kuo. "Remaining Useful Life Estimation-A Case Study on Soil Moisture Sensors." In Intelligent Information and Database Systems, 328–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54430-4_32.

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Si, Xiao-Sheng, Zheng-Xin Zhang, and Chang-Hua Hu. "An Adaptive Remaining Useful Life Estimation Approach with a Recursive Filter." In Springer Series in Reliability Engineering, 73–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54030-5_4.

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Lim, Reuben, and David Mba. "Fault Detection and Remaining Useful Life Estimation Using Switching Kalman Filters." In Lecture Notes in Mechanical Engineering, 53–64. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09507-3_6.

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Mabrouk, Nabila, Med Hedi Moulahi, and Fayçal Ben Hmida. "Degradation Process Analysis and Remaining Useful Life Estimation in a Control System." In Smart Innovation, Systems and Technologies, 49–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21009-0_5.

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Conference papers on the topic "Remaining useful life estimation"

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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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Wang, Qiyao, Shuai Zheng, Ahmed Farahat, Susumu Serita, and Chetan Gupta. "Remaining Useful Life Estimation Using Functional Data Analysis." In 2019 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2019. http://dx.doi.org/10.1109/icphm.2019.8819420.

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Heimes, Felix O. "Recurrent neural networks for remaining useful life estimation." In 2008 International Conference on Prognostics and Health Management (PHM). IEEE, 2008. http://dx.doi.org/10.1109/phm.2008.4711422.

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Wang, Haikun, Yu Liu, Zheng Liu, Zhonglai Wang, and Hong-Zhong Huang. "Remaining useful life estimation for degradation and shock processes." In 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). IEEE, 2013. http://dx.doi.org/10.1109/qr2mse.2013.6625917.

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Wang, Qiyao, Ahmed Farahat, Chetan Gupta, and Haiyan Wang. "Health Indicator Forecasting for Improving Remaining Useful Life Estimation." In 2020 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2020. http://dx.doi.org/10.1109/icphm49022.2020.9187047.

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You, Yaqian, Jianbin Sun, Jiang Jiang, Kewei Yang, and Bingfeng Ge. "RIMER based Remaining Useful Life Estimation of Aero-Engine*." In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2019. http://dx.doi.org/10.1109/smc.2019.8914403.

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Jensen, William R., Elias G. Strangas, and Shanelle N. Foster. "Online estimation of remaining useful life of stator insulation." In 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). IEEE, 2017. http://dx.doi.org/10.1109/demped.2017.8062421.

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Zhang, Yini, Yuanxiang Li, Yuxuan Zhang, and Lei Jia. "Distance-Based Embedding Learning for Remaining Useful Life Estimation." In 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai). IEEE, 2020. http://dx.doi.org/10.1109/phm-shanghai49105.2020.9280982.

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Tajiani, Bahareh, Jørn Vatn, and Viggo Gabriel Borg Pedersen. "Remaining Useful Life Estimation Using Vibration-based Degradation Signals." In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Singapore: Research Publishing Services, 2020. http://dx.doi.org/10.3850/978-981-14-8593-0_4793-cd.

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Reports on the topic "Remaining useful life estimation"

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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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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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