A survey on anomaly detection for technical systems using LSTM networks
Anomalies represent deviations from the intended system operation and can lead to
decreased efficiency as well as partial or complete system failure. As the causes of …
decreased efficiency as well as partial or complete system failure. As the causes of …
A survey of predictive maintenance: Systems, purposes and approaches
This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …
with emphasis on system architectures, purposes and approaches. In industry, any outages …
Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities
Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS).
However, due to the increasing complexity of CPSs and more sophisticated attacks …
However, due to the increasing complexity of CPSs and more sophisticated attacks …
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 …
A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of US
W Qiao, W Liu, E Liu - Energy, 2021 - Elsevier
The prediction model's performance in view of the wavelet transform (WT) is affected
because the wavelet basis function (WBF) and its orders and layers are determined …
because the wavelet basis function (WBF) and its orders and layers are determined …
A review of data-driven machinery fault diagnosis using machine learning algorithms
J Cen, Z Yang, X Liu, J Xiong, H Chen - Journal of Vibration Engineering & …, 2022 - Springer
Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …
driven machinery fault diagnosis based on machine learning algorithms, and provide …
Digitalisation and innovation in the steel industry in Poland—Selected tools of ICT in an analysis of statistical data and a case study
B Gajdzik, R Wolniak - Energies, 2021 - mdpi.com
Digital technologies enable companies to build cyber-physical systems (CPS) in Industry
4.0. In the increasingly popular concept of Industry 4.0, an important research topic is the …
4.0. In the increasingly popular concept of Industry 4.0, an important research topic is the …
Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads
M Qiao, S Yan, X Tang, C Xu - Ieee Access, 2020 - ieeexplore.ieee.org
To research the problems of the rolling bearing fault diagnosis under different noises and
loads, a dual-input model based on a convolutional neural network (CNN) and long-short …
loads, a dual-input model based on a convolutional neural network (CNN) and long-short …
A review on deep learning applications in prognostics and health management
Deep learning has attracted intense interest in Prognostics and Health Management (PHM),
because of its enormous representing power, automated feature learning capability and best …
because of its enormous representing power, automated feature learning capability and best …
Prognostics and health management of industrial assets: Current progress and road ahead
L Biggio, I Kastanis - Frontiers in Artificial Intelligence, 2020 - frontiersin.org
Prognostic and Health Management (PHM) systems are some of the main protagonists of
the Industry 4.0 revolution. Efficiently detecting whether an industrial component has …
the Industry 4.0 revolution. Efficiently detecting whether an industrial component has …