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Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Pangun Park, Piergiuseppe Di Marco, Hyejeon Shin, Junseong Bang
2019 Sensors  
The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults.  ...  It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis.  ...  The proposed approach combines an autoencoder to detect rare events and a long short-term memory (LSTM) network to identify the types of faults.  ... 
doi:10.3390/s19214612 pmid:31652821 pmcid:PMC6866134 fatcat:mboe65xzkbakhgl5cp43folivq

Deep Learning Approaches to Aircraft Maintenance, Repair and Overhaul: A Review

Divish Rengasamy, Herve P. Morvan, Grazziela P. Figueredo
2018 2018 21st International Conference on Intelligent Transportation Systems (ITSC)  
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  ...  , Convolutional Neural Networks and Deep Belief Networks.  ...  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

Machine Learning-based Anomaly Detection in Optical Fiber Monitoring

Khouloud Abdelli, Joo Yeon Cho, Carsten Tropschug, Stephan Pachnicke
2022 Journal of Optical Communications and Networking  
adopted for fault diagnosis and localization once an anomaly is detected by the autoencoder.  ...  The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm, whereby the former is used for fault detection and the latter is  ...  This work has been performed in the framework of the CELTIC-NEXT project AI-NET-PROTECT (Project ID C2019/3-4), and it is partly funded by the German Federal Ministry of Education and Research (FKZ16KIS1279K  ... 
doi:10.1364/jocn.451289 fatcat:yrdtfcjvnbanzea6a2ccceknjy

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Wei Cui, Guoying Meng, Aiming Wang, Xinge Zhang, Jun Ding, M.Z. Naser
2021 Shock and Vibration  
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  ...  networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault  ...  Acknowledgments is research work was supported by the Fundamental Research Funds for the Central Universities (Grant no. 00/ 800015A353) and Langfang Science and Technology Support  ... 
doi:10.1155/2021/3083190 fatcat:4no2xr3f75hszivh7uhq3r2t6y

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection

Ariyo Oluwasanmi, Muhammad Umar Aftab, Edward Baagyere, Zhiguang Qin, Muhammad Ahmad, Manuel Mazzara
2021 Sensors  
Additionally, a variational autoencoder (VAE) and a long short-term memory (LSTM) network is designed to learn the Gaussian distribution of the generative reconstruction and time-series sequential data  ...  Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios.  ...  Long Short-Term Memory Model The data is framed as a time-series sequence to sequentially analyze the time steps using a gated RNN.  ... 
doi:10.3390/s22010123 pmid:35009666 pmcid:PMC8747546 fatcat:glnwwinczjd23hbmyy2gtvpkwm

ML-based Anomaly Detection in Optical Fiber Monitoring [article]

Khouloud Abdelli, Joo Yeon Cho, Carsten Tropschug
2022 arXiv   pre-print
The proposed methods include an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm for the fiber fault identification and localization.  ...  We propose a data driven approach for the anomaly detection and faults identification in optical networks to diagnose physical attacks such as fiber breaks and optical tapping.  ...  Acknowledgments This work has been performed in the framework of the CELTIC-NEXT project AI-NET-PROTECT (Project ID C2019/3-4), and it is partly funded by the German Federal Ministry of Education and Research  ... 
arXiv:2202.11756v1 fatcat:kgsoyjvyb5fafpjhzp3qxujdpi

A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

Omar AlShorman, Muhammad Irfan, Nordin Saad, D. Zhen, Noman Haider, Adam Glowacz, Ahmad AlShorman, Yongfang Zhang
2020 Shock and Vibration  
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  ...  Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM.  ...  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 recurrent neural network and feature-transfer  ... 
doi:10.1155/2020/8843759 fatcat:h4zyvhct6nb7lpsj7j5f3yror4

A novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

Moath Alrifaey, Wei Hong Lim, Chun Kit Ang
2021 IEEE Access  
INDEX TERMS Deep learning (DL), fault detection, long short-term memory (LSTM), oil and gas plant, recurrent neural networks (RNN), stacked autoencoders (SAE).  ...  on the long short term memory-recurrent neural networks (RNN-LSTM), stacked autoencoders (SAE), and particle swarm optimization (PSO) techniques.  ...  FAULT DETECTION USING DL FRAMEWORK The RNN-LSTM approach was applied in this stage to learn long term dependencies in data of time series to assist in detecting the faults efficiently.  ... 
doi:10.1109/access.2021.3055427 fatcat:pm2fyeqburfwzlozh66bvtad4u

Diagnosis Model of Motor Fault of Precooled Air Conditioning Unit Based on Multivariable LSTM

Xiu-xiu SUN, Chang-lun ZHANG, Cui-wen ZHANG, Qiang HE, Ying-bo ZHANG
2019 DEStech Transactions on Computer Science and Engineering  
In this paper, a PAU motor fault diagnosis model is constructed based on long short-term memory neural network (LSTM) combined with in-depth learning technology.  ...  LSTM method is used to predict the motor shell temperature, and the motor fault detection and diagnosis are carried out according to the predicted residual threshold.  ...  LSTM Network Long short-term memory network (LSTM) is an improved structure of RNN, which is suitable for dealing with the problem of very long time interval and delay in time series prediction.  ... 
doi:10.12783/dtcse/cscme2019/32567 fatcat:sajwlb47sbasjehn5uainz7de4

Hierarchical Deep Recurrent Neural Network based Method for Fault Detection and Diagnosis [article]

Piyush Agarwal, Jorge Ivan Mireles Gonzalez, Ali Elkamel, Hector Budman
2020 arXiv   pre-print
Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis.  ...  A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants.  ...  Long-Short Term Memory (LSTM) Units The LSTM unit is composed of three gated units and a memory cell [20] .  ... 
arXiv:2012.03861v1 fatcat:l2z4ensfhbc73prnujriuzj6dq

Deep Learning based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Majdi Mansouri, Mohamed Trabelsi, Hazem Nounou, Mohamed Nounou
2021 IEEE Access  
INDEX TERMS Fault Diagnosis, Deep Learning, Photovoltaic Systems.  ...  Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD).  ...  At present, the most commonly used RNNs are Long Short-Term Memory networks (LSTM) and Gated Recurrent Unit (GRU) networks.  ... 
doi:10.1109/access.2021.3110947 fatcat:kuzjpcxanbc5dkilzprg76byoy

Review of Vibration-Based Structural Health Monitoring Using Deep Learning

Gyungmin Toh, Junhong Park
2020 Applied Sciences  
This review provides a summary of studies applying machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies.  ...  When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure.  ...  [130] proposed a convolutional bidirectional long short-term memory (CBLSTM) network that features both a CNN and long short-term memory (LSTM). Local information is extracted by the CNN.  ... 
doi:10.3390/app10051680 fatcat:4vgiycrznvgcjfsv6fshlc3seq

Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study

Jinlin Zhu, Muyun Jiang, Zhong Liu
2021 Sensors  
First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains.  ...  This work considers industrial process monitoring using a variational autoencoder (VAE).  ...  For this reason, two modern recurrent units, called the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), will be considered in this work.  ... 
doi:10.3390/s22010227 pmid:35009769 pmcid:PMC8749793 fatcat:zjj4wpp4gze6thntvu6ypoz2ga

A Novel Generative Method for Machine Fault Diagnosis

Zhipeng Dong, Yucheng Liu, Jianshe Kang, Shaohui Zhang, Haidong Shao
2022 Journal of Sensors  
Deep learning is widely used in fault diagnosis of mechanical equipment and has achieved good results.  ...  ; finally, combine with the pseudosamples, the deep learning method is training for machine fault diagnosis.  ...  Such as recurrent neural networks (RNN) [5] , autoencoder(AE) [6, 7] , long short-term memory (LSTM) [8] , deep belief network (DBN) [9] , and convolutional neural network (CNN) [10] , the advantage  ... 
doi:10.1155/2022/5420478 fatcat:abvmkrujp5f4dh62evl7pldehi

Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

Yuanyuan Yang, Md Muhie Menul Haque, Dongling Bai, Wei Tang
2021 Energies  
their use in detecting faults of electric motors.  ...  This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN), and highlights  ...  According to the literature [76] , the typical RNN has the problem of gradient disappearance or gradient explosion, which prevents it from using information from the past; therefore, a long-and short-term  ... 
doi:10.3390/en14217017 fatcat:fac5wki2tnabbpxgcsu4lm4kku
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