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Physics-based Remaining Useful Life Prediction for Aircraft Engine Bearing Prognosis Nathan Bolander 1 Hai Qiu 2 Neil Eklund 2 Ed Hindle 3 Taylor Rosenfeld 3 1 Sentient Corporation Idaho Falls Idaho 83404 USA nbolandersentientscience 2 GE Global Research Niskayuna New York 12309 USA qiuresearch ge eklundcrd ge
Remaining useful life (RUL) prediction is a process using prediction methods to forecast the future performance of components or systems and obtain the time left before them loses its operation ability Knowing the RUL of a system is essential for maintenance decision making and contingency mitigation
An accurate remaining useful life (RUL) prediction method is significant to optimize the lithium‐ion batteries' performances in an intelligent battery management system Since the construction of battery models and the initialization of algorithms require a large amount of data it is difficult for conventional methods to guarantee the RUL
Research Article Remaining Useful Life Prediction of Rolling Bearings Using PSR JADE and Extreme Learning Machine YongbinLiu 1 2 BingHe 1 FangLiu 1 2 SiliangLu 2 YileiZhao 1 andJiwenZhao 2 Department of Mechanical Engineering Anhui University Hefei China
Jan 03 2020Accurate prediction of remaining useful life (RUL) plays an important role in reducing the probability of accidents and lessening the economic loss However traditional model-based methods for RUL are not suitable when operating conditions and fault models are complicated
prediction of lithium-ion batteries The useful life of a battery is defined by the time until which a battery is able to maintain a minimum charge capacity when fully charged In this experiment data were collected at fixed time intervals (every four weeks) therefore RUL is calculated as the time remaining
Deep-learning-in-PHM Deep learning in PHM Deep learning in fault diagnosis Deep learning in remaining useful life prediction The purpose of this repository is to collect the application research of deep learning in PHM field collect and organize the open-source algorithm resources and provide a platform for researchers to learn and communicate
Why is the Remaining Useful Life Prediction Uncertain? Shankar Sankararaman1 and Kai Goebel2 1 SGT Inc NASA Ames Research Center Moffett Field CA 94035 USA shankar sankararamannasa gov 2 NASA Ames Research Center Moffett Field CA 94035 USA kai goebelnasa gov ABSTRACT This paper discusses the significance and interpretation of
A trend prediction method based on the Pchip-EEMD-GM(1 1) to predict the remaining useful life (RUL) of rolling bearings was proposed in this paper Firstly the dimension of the extracted features was reduced by the KPCA dimensionality reduction method and the WPHM model parameters were estimated via the kernel principal components Secondly the hazard rate was calculated at each time
Centralized Maintenance Time Prediction Algorithm for Freight Train Wheels Based on Remaining Useful Life Prediction Hongmei Shi 1 2 Jinsong Yang 1 2 and Jin Si 1 2 1 School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing 100044 China
Remaining useful life prediction of rolling element bearings using degradation feature based on amplitude decrease at specific frequencies Dawn An1 Joo-Ho Choi2 and Nam H Kim3 Abstract This research presents a new method of degradation feature extraction to predict remaining useful life the remaining time to the maintenance of rolling
The remaining useful life (RUL) of the unit indicates its ability of surviving the operation in the future When the failure indication (degradation) has been detected it is essential to estimate the RUL accurately for making a timely maintenance decision for failure avoidance
Echo State Network for the Remaining Useful Life Prediction of a Turbofan Engine Marco Rigamonti1 Piero Baraldi2 Enrico Zio3 Indranil Roychoudhury4 Kai Goebel5 and Scott Poll6 1 2 3Energy Department Politecnico di Milano Via Ponzio 34/3 Milan 20133 Italy marcomichael rigamontipolimi piero baraldipolimi
Baohua Mo is a Master candidate in the School of Automation Science and Electrical Engineering at Beihang University Beijing China He received his Bachelor's degree from the Ecole Centrale Pekin at Beihang University in 2015 His research interests focus on remaining useful life prediction
Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems Among the existing methods for RUL prediction the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling
Furthermore such information can be of great importance to the maintenance staff e g unplanned shutdowns can be avoided Relatively little work has been done in the field of remaining useful life (RUL) prediction of SOFCs The majority of work employs stack/cell voltage as a direct link for RUL predictions
Jan 15 2015Estimation of remaining useful life (RUL) is helpful to manage life cycles of machines and to reduce maintenance cost Support vector machine (SVM) is a promising algorithm for estimation of RUL because it can easily process small training sets and multi-dimensional data Many SVM based methods have been proposed to predict RUL of some key components
replaced In recent years a lot of research has been conducted on battery reliability and prognosis espe-cially the remaining useful life prediction of the lithium-ion batteries Particle filter (PF) is an effective method for sequential signal processing It has been used in many areas including computer vision tar-get tracking and robotics
Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function This lack of performance is most often a failure beyond which the system can no longer be used to meet desired performance The predicted time then becomes the remaining useful life (RUL) which is an important concept in decision making for
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
Jul 03 2019For advanced attitude control of the satellite reaction wheels are used which are actuated by motor Fault detection and prediction of Remaining Useful Life (RUL) of the motor is of great importance This study aims to demonstrate health management of reaction wheel motor in satellites by estimating the RUL of the damping coefficient Multi-scale Extended Kalman Filter (EKF) is employed
Feb 18 2009Predict Remaining Useful Life – An Automated Method to Identify Prognostic Parameters I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy with a major in Nuclear Engineering
The traditional remaining useful life prediction methods need to study the mechanism failure of equipment and the vibration signals can easily be submerged by the noise in the actual operation in order to solve these problems the methods of Trajectory similarity based prediction (TSBP) and condition monitoring based on lubricant information are proposed in this paper
[Research on engine remaining useful life prediction based on oil spectrum analysis and particle filtering] (SOA) is an important technique for machine state monitoring fault diagnosis and prognosis and SOA based remaining useful life(RUL) prediction has an advantage of finding out the optimal maintenance strategy for machine system
prognostics is to estimate remaining useful life (RUL) of some key components [3] In the field of prognostics RUL is an important concept that means the residual useful life on an asset at a particular time [4 5] A prognostic model predicts RUL of a component by assessing its degradation level from its expected normal health condition
Deep-learning-in-PHM Deep learning in PHM Deep learning in fault diagnosis Deep learning in remaining useful life prediction The purpose of this repository is to collect the application research of deep learning in PHM field collect and organize the open-source algorithm resources and provide a platform for researchers to learn and communicate
Remaining Useful Life Prediction through Failure Probability Computation for Condition-based Prognostics Shankar Sankararaman1 1 SGT Inc NASA Ames Research Center Moffett Field CA 94035 USA shankar sankararamannasa gov ABSTRACT The key goal in prognostics is to predict the remaining use-ful life (RUL) of engineering systems in order to
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