Deep Learning–Based Fault Identification Testing Experiment for Bellows Valves DOI Creative Commons
Jianwen Guo, Yuwei Cai,

Zihan Chen

и другие.

Science and Technology of Nuclear Installations, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

This study focuses on the fault identification of pneumatic bellows valve. valve plays an essential role in regulating system pressure to ensure smooth progress tritium removal process. To conduct research health status and timely identify potential faults anomalies, we developed a dedicated experimental platform assessed performance various deep learning models, including recurrent neural networks (RNNs), single‐layer long short–term memory (LSTMs), double‐layer LSTM, multilayer gated unit (GRU), bidirectional GRU, identification. The outcomes reveal that RNN GRU models exhibit superior terms accuracy model fit, particularly scenarios involving normal operations, leakage faults, head contact faults. These findings offer new perspectives methodologies for detection prevention contributing operational stability safety valves.

Язык: Английский

Switch ON/OFF learning of one-dimensional convolutional neural network and one-dimensional generative adversarial network for fault detection DOI

Seunghwan Song,

Kyuchang Chang,

Cheolsoon Park

и другие.

Journal of Intelligent Manufacturing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

Язык: Английский

Процитировано

0
Lightweight anomaly detection in federated learning via separable convolution and convergence acceleration DOI
Bin Jiang, Guangfeng Wang, Xuerong Cui

и другие.

Internet of Things, Год журнала: 2025, Номер unknown, С. 101518 - 101518

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Online prognostic failure AIoT system for industrial generators maintenance service based two-stage deep learning algorithm DOI
Da-Thao Nguyen, Phuong Nguyen Thanh, Ming-Yuan Cho

и другие.

Control Engineering Practice, Год журнала: 2025, Номер 157, С. 106263 - 106263

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

0
Cloud-based AIoT intelligent infrastructure for firefighting pump fault diagnosis-based hybrid CNN-GRU deep learning technique DOI
Da-Thao Nguyen, Phuong Nguyen Thanh, Ming-Yuan Cho

и другие.

The Journal of Supercomputing, Год журнала: 2025, Номер 81(3)

Опубликована: Фев. 4, 2025

Язык: Английский

Процитировано

0
Fault Detection in Steel Belts of Tires Using Magnetic Sensors and Different Deep Learning Models DOI Creative Commons
Sercan Yalçın

Firat University Journal of Experimental and Computational Engineering, Год журнала: 2025, Номер 4(1), С. 85 - 99

Опубликована: Фев. 18, 2025

Tire failures pose significant safety risks, necessitating advanced inspection techniques. This research investigates the application of magnetic sensors and deep learning for detecting defects in steel belts tires. It was aim to develop a robust accurate fault detection system by measuring field variations caused defects. In this study, image sensor circuit had been designed then images obtained from it have classified as none, crack, delamination type belt errors. Various models their hybrid architectures, were explored compared. Experimental results demonstrate that all exhibit strong performance, with Transformer model achieving highest accuracy 96.12%. The developed offers potential solution improving tire reducing maintenance costs industries.

Язык: Английский

Процитировано

0
A Mold Damage Monitoring Algorithm for Power Metallurgy Molding Machines Using Bidirectional Long Short-Term Memory on an Internet of Things Platform DOI Creative Commons

Hao-Pu Lin,

Yuan-Chieh Chen,

Chin‐Chuan Han

и другие.

Sensors, Год журнала: 2025, Номер 25(7), С. 2143 - 2143

Опубликована: Март 28, 2025

In this paper, an analysis and monitoring algorithm is proposed for mold health evaluation using vibration data. Two inertial measurement units (IMUs) embedded system are first used to acquire data from a powder metallurgy molding machine. These collected on Internet of Things (IoT) platform the Message Queueing Telemetry Transport (MQTT) protocol. For analysis, signal Z axis segmented label contact section upper middle molds, corresponding stamping friction X, Y, axes extracted. Using only historical normal stamping, Bidirectional Long Short-Term Memory (Bi-LSTM) model with attention mechanism trained predict vibrations several minutes in advance. By comparing predicted observed at current time, mean square errors (MSEs) calculated evaluate status mold. Several ablation experiments were conducted assess performance model. The average MSE values samples abnormal smaller than 0.5 larger 1.0, respectively. experimental results confirm that prediction indicators can effectively notify operators An early warning damage was successfully implemented, enhancing predictive maintenance.

Язык: Английский

Процитировано

0
An incorporation of metaheuristic algorithm and two-stage deep learnings for fault classified framework for diesel generator maintenance DOI
Phuong Nguyen Thanh

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 151, С. 110688 - 110688

Опубликована: Апрель 6, 2025

Язык: Английский

Процитировано

0
A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study DOI Creative Commons

Emanuel Krzysztoń,

Izabela Rojek, Dariusz Mikołajewski

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(24), С. 11545 - 11545

Опубликована: Дек. 11, 2024

The growth of the Internet Things (IoT) and its integration with Industry 4.0 5.0 are generating new security challenges. One key elements IoT systems is effective anomaly detection, which identifies abnormal behavior in devices or entire systems. This paper presents a comprehensive overview existing methods for detection networks using machine learning (ML). A detailed analysis various ML algorithms, both supervised (e.g., Random Forest, Gradient Boosting, SVM) unsupervised Isolation Autoencoder), was conducted. results tests conducted on popular datasets (IoT-23 CICIoT-2023) were collected analyzed detail. performance selected algorithms evaluated commonly used metrics (Accuracy, Precision, Recall, F1-score). experimental showed that Forest Autoencoder highly detecting anomalies. article highlights importance appropriate data preprocessing to improve accuracy. Furthermore, limitations centralized approach context distributed discussed. also potential directions future research field IoT.

Язык: Английский

Процитировано

2
Deep Learning–Based Fault Identification Testing Experiment for Bellows Valves DOI Creative Commons
Jianwen Guo, Yuwei Cai,

Zihan Chen

и другие.

Science and Technology of Nuclear Installations, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

This study focuses on the fault identification of pneumatic bellows valve. valve plays an essential role in regulating system pressure to ensure smooth progress tritium removal process. To conduct research health status and timely identify potential faults anomalies, we developed a dedicated experimental platform assessed performance various deep learning models, including recurrent neural networks (RNNs), single‐layer long short–term memory (LSTMs), double‐layer LSTM, multilayer gated unit (GRU), bidirectional GRU, identification. The outcomes reveal that RNN GRU models exhibit superior terms accuracy model fit, particularly scenarios involving normal operations, leakage faults, head contact faults. These findings offer new perspectives methodologies for detection prevention contributing operational stability safety valves.

Язык: Английский

Процитировано

0