Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …
systems continues to generate massive amounts of data. Many approaches have been …
Anomaly detection for IoT time-series data: A survey
Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …
the identification of novel or unexpected observations or sequences within the data being …
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 …
EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis
M Žvokelj, S Zupan, I Prebil - Journal of Sound and Vibration, 2016 - Elsevier
A novel multivariate and multiscale statistical process monitoring method is proposed with
the aim of detecting incipient failures in large slewing bearings, where subjective influence …
the aim of detecting incipient failures in large slewing bearings, where subjective influence …
Comparing PCA-based fault detection methods for dynamic processes with correlated and Non-Gaussian variables
MA de Carvalho Michalski, GFM de Souza - Expert Systems with …, 2022 - Elsevier
Maintenance strategies have been playing an increasingly important role in improving
engineering systems' performance, supporting the growth of availability and reliability, and …
engineering systems' performance, supporting the growth of availability and reliability, and …
On cross-domain feature fusion in gearbox fault diagnosis under various operating conditions based on transfer component analysis
J Xie, L Zhang, L Duan, J Wang - 2016 ieee international …, 2016 - ieeexplore.ieee.org
This paper addresses the cross-domain feature extraction and fusion from time-domain and
frequency-domain with spectrum envelop preprocessing and time domain synchronization …
frequency-domain with spectrum envelop preprocessing and time domain synchronization …
[HTML][HTML] Prognostics and health management in nuclear power plants: An updated method-centric review with special focus on data-driven methods
In a carbon-constrained world, future uses of nuclear power technologies can contribute to
climate change mitigation as the installed electricity generating capacity and range of …
climate change mitigation as the installed electricity generating capacity and range of …
Multiscale dynamic feature learning for quality prediction based on hierarchical sequential generative network
In industrial processes, long short-term memory (LSTM) is usually used for temporal
dynamic modeling of soft sensor. The process data usually have various temporal …
dynamic modeling of soft sensor. The process data usually have various temporal …
Remain useful life prediction of rolling bearings based on exponential model optimized by gradient method
Remaining useful life (RUL) using exponential model (EM) prediction has been a hot
research topic in the construction of prognostics health management (PHM) systems …
research topic in the construction of prognostics health management (PHM) systems …
[HTML][HTML] Advances in fault detection and diagnosis for thermal power plants: A review of intelligent techniques
Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their
safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs …
safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs …