[HTML][HTML] A systematic review of data fusion techniques for optimized structural health monitoring
Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
Challenges and opportunities of AI-enabled monitoring, diagnosis & prognosis: A review
Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …
prognosis, and health management, occupies an increasingly important position in reducing …
Deep residual shrinkage networks for fault diagnosis
M Zhao, S Zhong, X Fu, B Tang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
This article develops new deep learning methods, namely, deep residual shrinkage
networks, to improve the feature learning ability from highly noised vibration signals and …
networks, to improve the feature learning ability from highly noised vibration signals and …
Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals
RFR Junior, IA dos Santos Areias, MM Campos… - Measurement, 2022 - Elsevier
Fault detection and diagnosis in time series data are becoming mainstream in most
industrial applications since the increase of monitoring sensors in machinery. Traditional …
industrial applications since the increase of monitoring sensors in machinery. Traditional …
A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning
Limited condition monitoring data are recorded with label information in practice, which
make the fault identification task more challenging. A semi-supervised learning (SSL) …
make the fault identification task more challenging. A semi-supervised learning (SSL) …
Failure prognosis and applications—A survey of recent literature
Fault diagnosis and prognosis are some of the most crucial functionalities in complex and
safety-critical engineering systems, and particularly fault diagnosis, has been a subject of …
safety-critical engineering systems, and particularly fault diagnosis, has been a subject of …
Efficient automated disease diagnosis using machine learning models
Recently, many researchers have designed various automated diagnosis models using
various supervised learning models. An early diagnosis of disease may control the death …
various supervised learning models. An early diagnosis of disease may control the death …
Deep learning in smart grid technology: A review of recent advancements and future prospects
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …
a promising landscape for high grid reliability and efficient energy management. This …
Normalized conditional variational auto-encoder with adaptive focal loss for imbalanced fault diagnosis of bearing-rotor system
The distribution of the health data monitored from mechanical system in the industries is
class imbalanced mainly. The amount of monitoring data for the normal condition is far more …
class imbalanced mainly. The amount of monitoring data for the normal condition is far more …
Semisupervised graph convolution deep belief network for fault diagnosis of electormechanical system with limited labeled data
The labeled monitoring data collected from the electromechanical system is limited in the
real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory …
real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory …