Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system.
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 d...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315917 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832540282433830912 |
|---|---|
| author | Yuan Wang Shaolin Hu |
| author_facet | Yuan Wang Shaolin Hu |
| author_sort | Yuan Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cb4c1abcd1a444a29f24d77a8300c824 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-cb4c1abcd1a444a29f24d77a8300c8242025-02-05T05:31:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031591710.1371/journal.pone.0315917Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system.Yuan WangShaolin HuCentrifugal 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.https://doi.org/10.1371/journal.pone.0315917 |
| spellingShingle | Yuan Wang Shaolin Hu Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. PLoS ONE |
| title | Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. |
| title_full | Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. |
| title_fullStr | Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. |
| title_full_unstemmed | Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. |
| title_short | Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. |
| title_sort | design and realization of compressor data abnormality safety monitoring and inducement traceability expert system |
| url | https://doi.org/10.1371/journal.pone.0315917 |
| work_keys_str_mv | AT yuanwang designandrealizationofcompressordataabnormalitysafetymonitoringandinducementtraceabilityexpertsystem AT shaolinhu designandrealizationofcompressordataabnormalitysafetymonitoringandinducementtraceabilityexpertsystem |