973 research outputs found
Predictive maintenance of rotational machinery using deep learning
This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability
Separating multiscale Battery dynamics and predicting multi-step ahead voltage simultaneously through a data-driven approach
Accurate prediction of battery performance under various ageing conditions is
necessary for reliable and stable battery operations. Due to complex battery
degradation mechanisms, estimating the accurate ageing level and
ageing-dependent battery dynamics is difficult. This work presents a
health-aware battery model that is capable of separating fast dynamics from
slowly varying states of degradation and state of charge (SOC). The method is
based on a sequence-to-sequence learning-based encoder-decoder model, where the
encoder infers the slowly varying states as the latent space variables in an
unsupervised way, and the decoder provides health-aware multi-step ahead
prediction conditioned on slowly varying states from the encoder. The proposed
approach is verified on a Lithium-ion battery ageing dataset based on real
driving profiles of electric vehicles.Comment: 6 pages, 10 figures, IEEE Vehicle Power and Propulsion confernce(IEEE
VPPC 2023
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Unsupervised anomaly detection in pressurized water reactor digital twins using autoencoder neural networks
Deep learning (DL), that is becoming quite popular for prediction and analysis of complex patterns in large amounts of data is used to investigate the safety behaviour of the nuclear plant items. This is achieved by using multiple layers of artificial neural networks to process and transform input data, allowing for the creation of highly accurate predictive models. Particularly to the aim the unsupervised machine learning approach and the digital twin concept in form of pressurized water reactor 2-loop simulator are used. This innovative methodology is based on neural network algorithm that makes capable to predict failures of plant structure, system, and components earlier than the activation of safety and emergency systems. Moreover, to match the objective of the study several scenarios of loss of cooling accident (LOCA) of different break size were simulated. To make the acquisition platform realistic, Gaussian noise was added to the input signals. The neural network has been fed by synthetic dataset provide by PCTRAN simulator and the efficiency in event identification was studied. Further, due to the very limited studies on the unsupervised anomaly detection by means of autoencoder neural networks applied for plant monitoring and surveillance, the methodology has been validated with experimental data from resonant test rig designed for fatigue testing of tubular components. The obtained results demonstrate the reliability and the efficiency of the methodology in detecting anomalous events prior the activation of safety system. Particularly, if the difference between the expected readings and the collected data goes beyond the predetermined threshold, then the anomalous event is identified, e.g., the model detected anomalies up to 38 min before the reactor scram intervention
Automated Website Fingerprinting through Deep Learning
Several studies have shown that the network traffic that is generated by a
visit to a website over Tor reveals information specific to the website through
the timing and sizes of network packets. By capturing traffic traces between
users and their Tor entry guard, a network eavesdropper can leverage this
meta-data to reveal which website Tor users are visiting. The success of such
attacks heavily depends on the particular set of traffic features that are used
to construct the fingerprint. Typically, these features are manually engineered
and, as such, any change introduced to the Tor network can render these
carefully constructed features ineffective. In this paper, we show that an
adversary can automate the feature engineering process, and thus automatically
deanonymize Tor traffic by applying our novel method based on deep learning. We
collect a dataset comprised of more than three million network traces, which is
the largest dataset of web traffic ever used for website fingerprinting, and
find that the performance achieved by our deep learning approaches is
comparable to known methods which include various research efforts spanning
over multiple years. The obtained success rate exceeds 96% for a closed world
of 100 websites and 94% for our biggest closed world of 900 classes. In our
open world evaluation, the most performant deep learning model is 2% more
accurate than the state-of-the-art attack. Furthermore, we show that the
implicit features automatically learned by our approach are far more resilient
to dynamic changes of web content over time. We conclude that the ability to
automatically construct the most relevant traffic features and perform accurate
traffic recognition makes our deep learning based approach an efficient,
flexible and robust technique for website fingerprinting.Comment: To appear in the 25th Symposium on Network and Distributed System
Security (NDSS 2018
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