Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system
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| Pubblicato in: | PLoS One vol. 20, no. 1 (Jan 2025), p. e0315917 |
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Public Library of Science
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| Accesso online: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0315917 |2 doi | |
| 035 | |a 3156421470 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
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| 100 | 1 | |a Wang, Yuan | |
| 245 | 1 | |a Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system | |
| 260 | |b Public Library of Science |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies. Additionally, it presents an intelligent system design method for fault tracing in compressors and localization of faults from different sources. This method starts from petrochemical big data and consists of three parts: fault dynamic knowledge graph construction, instrument data sliding fault-tolerant filtering, and the fusion and reasoning of fault dynamic knowledge graph and instrument data variation monitoring. The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)—SPE (Square Prediction Error)—CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results. | |
| 653 | |a Software | ||
| 653 | |a Deep learning | ||
| 653 | |a Fuzzy sets | ||
| 653 | |a Big Data | ||
| 653 | |a Expert systems | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Oil and gas industry | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Fault tolerance | ||
| 653 | |a Neural networks | ||
| 653 | |a Systems design | ||
| 653 | |a Gas industry | ||
| 653 | |a Localization | ||
| 653 | |a Natural gas industry | ||
| 653 | |a Monitoring | ||
| 653 | |a Fault detection | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Risk assessment | ||
| 653 | |a Petrochemicals | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Data compression | ||
| 653 | |a False alarms | ||
| 653 | |a Reinjection | ||
| 653 | |a Knowledge | ||
| 653 | |a Process controls | ||
| 653 | |a Support vector machines | ||
| 653 | |a Classification | ||
| 653 | |a Natural gas | ||
| 653 | |a Information processing | ||
| 653 | |a Algorithms | ||
| 653 | |a Anomalies | ||
| 653 | |a Tracing | ||
| 653 | |a Natural language processing | ||
| 653 | |a Centrifugal compressors | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Hu, Shaolin | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 1 (Jan 2025), p. e0315917 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3156421470/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3156421470/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3156421470/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |