Results 11 to 20 of about 10,623,511 (284)

A DLSTM-Network-Based Approach for Mechanical Remaining Useful Life Prediction

open access: yesSensors, 2022
Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, these ...
Yan Liu   +5 more
doaj   +2 more sources

Prediction of Tool Remaining Useful Life Based on NHPP-WPHM

open access: yesMathematics, 2023
A tool remaining useful life prediction method based on a non-homogeneous Poisson process and Weibull proportional hazard model (WPHM) is proposed, taking into account the grinding repair of machine tools during operation.
Yingzhi Zhang   +4 more
doaj   +2 more sources

Prognostics for electronics components of avionics - NASA IGBT accelerated ageing case study [PDF]

open access: yes, 2013
Insulate gate bipolar transistors (IGBTs) are widely used in electric vehicles, railway locomotive and new generation aircrafts, due to the IGBTs have advantages in small conduction resistance and small drive current.
Xie, Yuan'an
core   +7 more sources

A Hybrid Prognostic Methodology and its Application to Well-Controlled Engineering Systems [PDF]

open access: yes, 2015
This thesis presents a novel hybrid prognostic methodology, integrating physics-based and data-driven prognostic models, to enhance the prognostic accuracy, robustness, and applicability.
Eker, Ömer Faruk
core   +7 more sources

Remaining Useful Life Prediction Using Temporal Convolution with Attention

open access: yesAI, 2021
Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set.
Wei Ming Tan, T. Hui Teo
doaj   +1 more source

Data-driven remaining useful life prediction based on domain adaptation [PDF]

open access: yesPeerJ Computer Science, 2021
As an important part of prognostics and health management, remaining useful life (RUL) prediction can provide users and managers with system life information and improve the reliability of maintenance systems.
Bin cheng Wen   +5 more
doaj   +2 more sources

Predicting the remaining useful life of rolling element bearings [PDF]

open access: yes2018 IEEE International Conference on Industrial Technology (ICIT), 2018
Condition monitoring of rolling element bearings is of vital importance in order to keep the industrial wheels running. In wind industry this is especially important due to the challenges in practical maintenance. The paper presents an attempt to improve the capability of prediction of remaining useful life of rolling bearings. The approach is based on
Hooghoudt, Jan Otto   +4 more
openaire   +3 more sources

Model-based prediction of the remaining useful life of the machines [PDF]

open access: yesIFAC-PapersOnLine, 2016
Abstract Accurate prediction of the remaining useful life (RUL) of machines is becoming mandatory in exploiting the asset in an efficient and secure way by avoiding the unplanned downtimes. In this paper we present an approach to the RUL prediction developed for a shot blasting machine by analyzing the recordings from inexpensive vibrational sensors.
Pavle Boskoski   +3 more
openaire   +2 more sources

Automated Machine Learning for Remaining Useful Life Predictions

open access: yes2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023
Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL predictions are becoming prevalent over model-based approaches since no underlying physical knowledge of the engineering system is required.
Marc-André Zöller   +4 more
openaire   +3 more sources

jiaxiang-cheng/transformer-pytorch-remaining-useful-life-prediction: Pre-release with Minimum Implementation

open access: yes, 2021
Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021).
Jia-Xiang Cheng
core   +1 more source

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