Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Machinery prognostics is one of the major tasks in condition based maintenance (CBM),
which aims to predict the remaining useful life (RUL) of machinery based on condition …
which aims to predict the remaining useful life (RUL) of machinery based on condition …
Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems
A challenging problem in risk and reliability analysis of Complex Engineering Systems
(CES) is performing and updating risk and reliability assessments on the whole system with …
(CES) is performing and updating risk and reliability assessments on the whole system with …
Remaining useful life estimation using long short-term memory neural networks and deep fusion
Y Zhang, P Hutchinson, NAJ Lieven… - IEEE …, 2020 - ieeexplore.ieee.org
Estimation of Remaining Useful Life (RUL) is a crucial task in Prognostics and Health
Management (PHM) for condition-based maintenance of machinery. In order to transmit and …
Management (PHM) for condition-based maintenance of machinery. In order to transmit and …
Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions
C Che, H Wang, Q Fu, X Ni - Advances in Mechanical …, 2019 - journals.sagepub.com
Rolling bearings are the vital components of rotary machines. The collected data of rolling
bearing have strong noise interference, massive unlabeled samples, and different fault …
bearing have strong noise interference, massive unlabeled samples, and different fault …
A two-stage approach for predicting the remaining useful life of tools using bidirectional long short-term memory
C Liu, L Zhu - Measurement, 2020 - Elsevier
It is common to deal with the imbalanced data set when predicting the remaining useful life
(RUL) of tools. This paper adopts the oversampling method of adaptive synthetic sampling …
(RUL) of tools. This paper adopts the oversampling method of adaptive synthetic sampling …
Diagnosis of operational failures and on-demand failures in nuclear power plants: An approach based on dynamic Bayesian networks
Successful diagnosis of system failures in nuclear power plants plays a central role in
emergency response. Existing research focuses on diagnosis of operational failures that …
emergency response. Existing research focuses on diagnosis of operational failures that …
Estimating damage size and remaining useful life in degraded structures using deep learning-based multi-source data fusion
In this article, a new deep learning-based approach for online estimation of damage size
and remaining useful life of structures is presented. The proposed approach consists of …
and remaining useful life of structures is presented. The proposed approach consists of …
Predicting growth and interaction of multiple cracks in structural systems using Dynamic Bayesian Networks
K Zhang, M Collette - Marine Structures, 2022 - Elsevier
Digital twins have the potential to improve future health prognosis and operational safety of
engineering structures. However, few studies have attempted to validate such twins with …
engineering structures. However, few studies have attempted to validate such twins with …
Capsule Neural Networks for structural damage localization and quantification using transmissibility data
JF Barraza, EL Droguett, VM Naranjo… - Applied Soft Computing, 2020 - Elsevier
One of the current challenges in structural health monitoring (SHM) is to take the most
advantage of large amounts of data to deliver accurate damage measurements and …
advantage of large amounts of data to deliver accurate damage measurements and …
A common cause failure model for components under age-related degradation
This paper discusses component age-related degradation and failure initiated from a shared
cause and coupling factor (or mechanism) and the likelihood of the resulting common cause …
cause and coupling factor (or mechanism) and the likelihood of the resulting common cause …