التفاصيل البيبلوغرافية
| العنوان: |
Deep Learning-Based Prognostics and Health Management Model for Pilot-Operated Cryogenic Safety Valves. |
| المؤلفون: |
Kim M; Department of Computer and Information Engineering, Catholic University of Pusan, Busan 46252, Republic of Korea., Seong H; DH Controls Co., Ltd., Busan 46747, Republic of Korea., Kim D; Department of Computer and Information Engineering, Catholic University of Pusan, Busan 46252, Republic of Korea. |
| المصدر: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 12; Vol. 24 (6). Date of Electronic Publication: 2024 Mar 12. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: Basel, Switzerland : MDPI, c2000- |
| مواضيع طبية MeSH: |
Deep Learning*, Prognosis ; Reproducibility of Results ; Industry ; Longevity |
| مستخلص: |
This paper highlights the significance of safety and reliability in modern industries, particularly in sectors like petroleum and LNG, where safety valves play a critical role in ensuring system safety under extreme conditions. To enhance the reliability of these valves, this study aims to develop a deep learning-based prognostics and health management (PHM) model. Past empirical methods have limitations, driving the need for data-driven prediction models. The proposed model monitors safety valve performance, detects anomalies in real time, and prevents accidents caused by system failures. The research focuses on collecting sensor data, analyzing trends for lifespan prediction and normal operation, and integrating data for anomaly detection. This study compares related research and existing models, presents detailed results, and discusses future research directions. Ultimately, this research contributes to the safe operation and anomaly detection of pilot-operated cryogenic safety valves in industrial settings. |
| معلومات مُعتمدة: |
2021R1G1A1014389 National Research Foundation of Korea |
| فهرسة مساهمة: |
Keywords: PHM model; anomaly detection; data-driven prediction models; prognostics and health management; real-time monitoring; safety valves |
| تواريخ الأحداث: |
Date Created: 20240328 Date Completed: 20240329 Latest Revision: 20240330 |
| رمز التحديث: |
20240330 |
| مُعرف محوري في PubMed: |
PMC10975573 |
| DOI: |
10.3390/s24061814 |
| PMID: |
38544078 |
| قاعدة البيانات: |
MEDLINE |
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