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A New Approach for Thermal Vision based Fall Detection Using Residual Autoencoder
2020
International Journal of Intelligent Engineering and Systems
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Our proposed model uses autoencoder based on convolutional neural network, convolutional long short term memory (ConvLSTM) network, and residual connections to extract spatial and temporal features of ...
(DAE), convolutional autoencoder (CAE), and convolutional long short term memory autoencoder (CLSTMAE) introduced in the literature. ...
CAE), and convolutional long short term memory autoencoder (CLSTMAE) models. ...
doi:10.22266/ijies2020.0430.24
fatcat:xetpg7puibdkforq55hc3cb4ye
Deep Learning Approaches to Aircraft Maintenance, Repair and Overhaul: A Review
2018
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
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Although deep learning in general is not yet largely exploited for aircraft health, from our search, we identified four main architectures employed to MRO, namely, Deep Autoencoders, Long Short-Term Memory ...
Experiments using deep learning techniques, however, have demonstrated its usefulness in assisting on the analysis aircraft health data. ...
Yuan et al. [15]
Long Short-Term Memory
Fault diagnosis and remaining useful life estimation of
aero-engine
ElSaid et al. [16]
Long Short-Term Memory + Ant Colony Optimization [17]
Prognosis of excess ...
doi:10.1109/itsc.2018.8569502
dblp:conf/itsc/RengasamyMF18
fatcat:fdetlamp6vesngmpu7icccvqxe
Review of Vibration-Based Structural Health Monitoring Using Deep Learning
2020
Applied Sciences
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With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. ...
This review provides a summary of studies applying machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app10051680
fatcat:4vgiycrznvgcjfsv6fshlc3seq
Minimal-Configuration Anomaly Detection for IIoT Sensors
[article]
2021
arXiv
pre-print
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Recent advances in deep learning, especially long-short-term memory (LSTM) and autoencoders, offer promising methods for detecting anomalies in sensor data recordings. ...
Our preliminary results indicate that a single model can detect anomalies under various operating conditions on a four-dimensional data set without any specific feature engineering for each operating condition ...
EXPERIMENTAL SETUP We trained unsupervised machine learning models to detect the anomalies in our dataset by using autoencoders based on a fully connected deep neural network (DNN), long short-term memory ...
arXiv:2110.04049v1
fatcat:7crr3fugafavrifk6thxwaaghq
Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. ...
India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. ...
RNNs are capable of remembering inputs with the help of Long Short Term Memory (LSTM) units. ...
doi:10.35940/ijitee.f1111.0486s419
fatcat:5mtkrtx54ndtbn2oms6ij7s5xy
Deep Learning for Anomaly Detection: A Survey
[article]
2019
arXiv
pre-print
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We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. ...
The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. ...
CNN: Convolution Neural Networks, LSTM : Long Short Term Memory Networks AE: Autoencoders. ...
arXiv:1901.03407v2
fatcat:x3tb4ccxfvdkfo7k2y2oxhr7ly
Application of Rotating Machinery Fault Diagnosis Based on Deep Learning
2021
Shock and Vibration
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After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural ...
The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. ...
Researchers have developed improved models such as long short-term memory models (LSTM) and gated recurrent units (GRU) based on standard RNN to solve the shortcomings of RNN. ...
doi:10.1155/2021/3083190
fatcat:4no2xr3f75hszivh7uhq3r2t6y
Residual Convolution LSTM Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification
2021
Journal of Dynamics, Monitoring and Diagnostics
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., long short-term memory (LSTM) network, are gaining more attention because of their capability of capturing temporal dependence. ...
In RC-LSTM, a new ResNet-based convolution LSTM (Res-ConvLSTM) layer is stacked with convolution LSTM (ConvLSTM) layer to extract degradation representations from monitoring data. ...
Time series
multiple channel convolutional neural network
with attention-based long short-term memory for
predicting bearing remaining useful life[J].
Sensors, 2020, 20(1): 166. ...
doi:10.37965/jdmd.v2i2.43
fatcat:grq4dwkl2vcu3afjshyzvpj5ha
Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques
2023
Frontiers in Energy Research
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In the present study, three types of deep learning techniques, namely, stacked autoencoder (SAE) network, long short term memory (LSTM) network, and convolutional neural network (CNN) are applied to fault ...
The results show that the diagnosis accuracy based on SAE and LSTM networks is lower than 50%, while is higher than 68% when using CNN. ...
Long short term memory network Long short term memory network is developed based on Recurrent Neural Network (RNN), and it improve on the longterm information dependence of RNN. ...
doi:10.3389/fenrg.2022.1109214
fatcat:s6jnwm5mcjhalarorpf6pb36tu
A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders
2021
Sensors
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Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the ...
An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. ...
Data Availability Statement: Data available on request due to privacy restrictions.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21030972
pmid:33535642
pmcid:PMC7867153
fatcat:bdbuiwhadrhh7m4pni5xbl3uzy
Design of Intelligent Nursing System Based on Artificial Intelligence
2022
Journal of Sensors
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In view of the above background, this paper designs a set of intelligent nursing system for the elderly based on artificial intelligence (AI) algorithms, which mainly includes sensor terminals and AI processing ...
Among them, the sensor terminal mainly includes two parts: video monitoring and human biological signal monitoring. ...
Typical representatives of such methods are autoencoders, convolutional neural networks (CNN), long-short term memory (LSTM), generative adversarial networks (GAN), etc. [22] [23] [24] [25] . ...
doi:10.1155/2022/7427968
fatcat:uqrfalz7bjbwzef7n5yqswkgpm
A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
2020
Shock and Vibration
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Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. ...
The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial ...
Two-dimensional acoustic
frequency spectral imaging with a
transfer learning is discussed
(iii) e proposed method achieved an
average accuracy of 94.67%
[161]
Vibration
Long-/short-term memory ...
doi:10.1155/2020/8843759
fatcat:h4zyvhct6nb7lpsj7j5f3yror4
A Deep Learning model for Smart Manufacturing using Convolutional LSTM Neural Network Autoencoders
2020
IEEE Transactions on Industrial Informatics
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Index Terms-Convolutional long short-term memory (ConvLSTM), deep learning (DL), industry 4.0, stacked autoencoders, time-series forecasting. ...
Time-series forecasting is applied to many areas of smart factories, including machine health monitoring, predictive maintenance, and production scheduling. ...
Fig. 2 shows the basic structure of an LSTM memory cell having two distinct components-the long-term state component c (t) and the short-term state component h (t) . ...
doi:10.1109/tii.2020.2967556
fatcat:k4dvywpiqjdadatngrwwytfeeq
A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System
2022
Sensors
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In this paper, we propose a deep learning approach that consists of a two-stage process using an autoencoder and long short-term memory (LSTM) to detect anomalies in EPS sensor data. ...
An anomaly score is used to detect anomalies based on the reconstruction loss of the output. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s22228981
pmid:36433579
pmcid:PMC9699008
fatcat:cfretujzbrgxblhfiubbavavje
Fault Detection in a Wind Turbine Hydraulic Pitch System Using Deep Autoencoder Extracted Features
2022
Proceedings of the European Conference of the Prognostics and Health Management Society (PHME)
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The architecture of the Autoencoder is investigated regarding its efficiency on fault detection task. This way, effect of new extracted features on the performance of the classifier is presented. ...
In this study, a feature space of 49 features is available, referring to the condition of a hydraulic pitch system. ...
Possible other classifiers, from the Deep Learning field, may be investigated in the future such as 1D Convolutional Neural Network or Long Short-Term Memory network (LSTM). 𝑛 − ∑ 𝑎 𝑚 𝑦 𝑚 𝐾(𝑥 𝑛 ...
doi:10.36001/phme.2022.v7i1.3330
fatcat:zndadp2umjh5na5zogmwqjpura
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