Search Results - "LSTM Autoencoder"

The train collision load identification method combining LSTM autoencoder network and optimization iteration by He, Jiaxing, Xu, Ping, Xing, Jie, Yao, Shuguang, Hao, Guangxiang, Wang, Bo

“…Train collisions can cause serious casualties and property damage, and collision testing is a key means of evaluating the crashworthiness of trains. However,…”
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Unsupervised learning-based damage detection of mooring lines in floating bridges by Min, Seongi, Song, Jihun, Kim, Seungjun

“…Floating bridges offer a practical alternative to sea-crossing bridges in regions with deep water and poor seabed conditions. It consists of a superstructure…”
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Recurrent normalizing flow-based monitoring framework for cutter seal temperature sensors in earth pressure balance shield tunneling: A sensor validation approach by Loy-Benitez, Jorge, Lee, Je-Kyum, Song, Myung Kyu, Guerra, Fabian Cabrera, Lee, Sean Seungwon

“…[Display omitted] •Data-driven sensor validation promotes EPB safety by detecting faulty temperature sensors.•Hybrid LSTM-AE and RAF model captures temporal…”
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Neural network LSTM autoencoder model for early anomaly detection in geophysical soil monitoring by Bykov, Artem, Daurenbayeva, Nurkamilya, Mamanova, Symbat, Nurlanuly, Almas, Tleuova, Gaini

“…Monitoring soil conditions is a critical task in geotechnical engineering, as it directly affects the stability of buildings, transport infrastructure, and…”
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Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder by Wu, Ji-Yan, Wu, Min, Chen, Zhenghua, Li, Xiao-Li, Yan, Ruqiang

“…The remaining useful life (RUL) prediction plays a pivotal role in the predictive maintenance of industrial manufacturing systems. However, one major problem…”
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Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT by Nizam, Hussain, Zafar, Samra, Lv, Zefeng, Wang, Fan, Hu, Xiaopeng

“…The data produced by millions of connected devices and smart sensors in the Industrial Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous,…”
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Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers by Mallak, Ahlam, Fathi, Madjid

“…Anomaly occurrences in hydraulic machinery might lead to massive system shut down, jeopardizing the safety of the machinery and its surrounding human…”
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A deep learning approach for error detection and quantification in extrusion-based bioprinting by Bonatti, Amedeo Franco, Vozzi, Giovanni, Kai Chua, Chee, De Maria, Carmelo

“…Quality control in extrusion-based bioprinting (EBB) represents a crucial step to: i) reduce the trial-and-error process and associated material consumption,…”
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Data-feature-driven nonlinear process monitoring based on joint deep learning models with dual-scale by Yu, Jianbo, Yan, Xuefeng

“…The interactions among the gauged data in most exiting real-life cases are correlative inevitably given the complicated behavior of process systems, that is…”
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A control-oriented operation mode recognizing method using fuzzy evaluation and attention LSTM networks by Sun, Bei, Peng, Zhixuan, Dai, Juntao, Li, Yonggang

“…Operation mode recognition is a prerequisite for precise regulation of key performance indicators (KPIs) in industrial processes. However, system uncertainties…”
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Deep learning for online AC False Data Injection Attack detection in smart grids: An approach using LSTM-Autoencoder by Yang, Liqun, Zhai, You, Li, Zhoujun

“…The Power system is a crucial Cyber-Physical system and is prone to the False Data Injection Attack (FDIA). The existing FDIA detection mechanism focuses on DC…”
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Detecting structural anomalies of quadcopter UAVs based on LSTM autoencoder by Jeon, Seunghyeok, Kang, Jaeyun, Kim, Jiwon, Cha, Hojung

“…Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The…”
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Hybrid Cyber Defense Mechanism with PINN for Resilient and Reliable Control against Replay / FDI Attacks in DC Microgrid Systems by Machina, Venkata Siva Prasad, Koduru, Sriranga Suprabhath, Madichetty, Sreedhar, Mishra, Sukumar

“…With the growing reliance on DC microgrids (DC MGs) in critical infrastructure, securing them against sophisticated cyberattacks is essential. This study…”
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An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing by Shi, Zhangyue, Mamun, Abdullah Al, Kan, Chen, Tian, Wenmeng, Liu, Chenang

“…Additive manufacturing (AM) has gained increasing popularity in a large variety of mission-critical fields, such as aerospace, medical, and transportation. The…”
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Deep Learning Autoencoder Study on ECG Signals by Mochamad Reza, Dandi, Satria Mandala, Zaki, Salim M., Ming, Eileen Su Lee

“…Arrhythmia refers to an irregular heart rhythm resulting from disruptions in the heart's electrical activity. To identify arrhythmias, an electrocardiogram…”
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LSTM-Autoencoder Based Anomaly Detection Using Vibration Data of Wind Turbines by Lee, Younjeong, Park, Chanho, Kim, Namji, Ahn, Jisu, Jeong, Jongpil

“…The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines…”
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Data-Driven Detection of Stealth Cyber-Attacks in DC Microgrids by Takiddin, Abdulrahman, Rath, Suman, Ismail, Muhammad, Sahoo, Subham

“…Cyber-physical systems such as microgrids contain numerous attack surfaces in communication links, sensors, and actuators forms. Manipulating the communication…”
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GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis by Lee, Daehee, Choo, Hyunseung, Jeong, Jongpil

“…The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency…”
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Detecting failed tethers in submerged floating tunnels using an LSTM autoencoder and DNN algorithms by Min, Seongi, Jeong, Kiwon, Kim, Seungjun

“…This study proposes a two-step approach for detecting damaged tethers in submerged floating tunnels. The proposed method employs two different artificial…”
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Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis by Mumcuoglu, Mehmet E., Farea, Shawqi M., Unel, Mustafa, Mise, Serdar, Unsal, Simge, Cevik, Enes, Yilmaz, Metin, Koprubasi, Kerem

“…This study develops a novel semi-supervised approach for detecting Air Pressure System (APS) failures in Heavy-Duty Vehicles (HDVs) by exploiting two modern…”
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