Deep Neural Network for Valve Fault Diagnosis Integrating Multivariate Time-Series Sensor Data
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| Publicado en: | Actuators vol. 14, no. 2 (2025), p. 70 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3170834214 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2076-0825 | ||
| 024 | 7 | |a 10.3390/act14020070 |2 doi | |
| 035 | |a 3170834214 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231328 |2 nlm | ||
| 100 | 1 | |a Jeong, Eugene | |
| 245 | 1 | |a Deep Neural Network for Valve Fault Diagnosis Integrating Multivariate Time-Series Sensor Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Faults in valves that regulate fluid flow and pressure in industrial systems can significantly degrade system performance. In systems where multiple valves are used simultaneously, a single valve fault can reduce overall efficiency. Existing fault diagnosis methods struggle with the complexity of multivariate time-series data and unseen fault scenarios. To overcome these challenges, this study proposes a method based on a one-dimensional convolutional neural network (1D CNN) for diagnosing the location and severity of valve faults in a multi-valve system. An experimental setup was constructed with 17 sensors, including 8 pressure sensors at the inlets and outlets of 4 valves, 4 flow sensors, and 5 pressure sensors along the main pipe. Sensor data were collected to observe the sensor values corresponding to valve behavior, and foreign objects of varying sizes were inserted into the valves to simulate faults of different severities. These data were used to train and evaluate the proposed model. The proposed method achieved robust prediction accuracy (MAE: 0.0306, RMSE: 0.0629) compared to existing networks, performing on both trained and unseen fault severities. It identified the location of the faulty valve and quantified fault severity, demonstrating generalization capabilities. | |
| 653 | |a Pressure sensors | ||
| 653 | |a Datasets | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Water supply | ||
| 653 | |a Valves | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Nozzles | ||
| 653 | |a Support vector machines | ||
| 653 | |a Multivariate analysis | ||
| 653 | |a Fluid flow | ||
| 653 | |a Faults | ||
| 653 | |a Automation | ||
| 653 | |a Inlets | ||
| 653 | |a Time series | ||
| 700 | 1 | |a Jung-Hwan, Yang | |
| 700 | 1 | |a Soo-Chul Lim | |
| 773 | 0 | |t Actuators |g vol. 14, no. 2 (2025), p. 70 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3170834214/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3170834214/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3170834214/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |