A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator
The electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the availability and reliability of the electrical generation in the power industry. Therefore, extracting and diagnosing the fault features from the proc...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9337852/ |
| _version_ | 1819276827440447488 |
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| author | Moath Alrifaey Wei Hong Lim Chun Kit Ang |
| author_facet | Moath Alrifaey Wei Hong Lim Chun Kit Ang |
| author_sort | Moath Alrifaey |
| collection | DOAJ |
| description | The electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the availability and reliability of the electrical generation in the power industry. Therefore, extracting and diagnosing the fault features from the process signals are useful to diagnose the status of the machine. Though, a common challenge in many applied applications is the practical knowledge about the risk of failure or historical records, which is totally unlabeled and difficult to be identified by traditional fault approaches. Hence, in the present study, a novel deep learning (DL) framework is proposed to fill the gap by balancing the three stages of fault feature extraction, fault detection, and parameter optimization based on the long short term memory- recurrent neural networks (RNN- LSTM), stacked autoencoders (SAE), and particle swarm optimization (PSO) techniques. The suggested framework focuses on failure detection through a sequence of numerous features for the unlabeled historical data and unknown anomaly. To validate the effectiveness of the proposed DL framework, an experiment for failure detection of the electrical generator was conducted for the data of risky environment at Yemen oil and gas plant. The experimental results compared with the earlier studies validate that, the DL framework can address the faults for vibration signals of the electrical generator in a well- diagnosis performance effectively. |
| first_indexed | 2024-12-23T23:46:24Z |
| format | Article |
| id | doaj.art-c3504e96c090479ebf2c55186177f9d8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:46:24Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c3504e96c090479ebf2c55186177f9d82022-12-21T17:25:29ZengIEEEIEEE Access2169-35362021-01-019214332144210.1109/ACCESS.2021.30554279337852A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas GeneratorMoath Alrifaey0https://orcid.org/0000-0001-5666-017XWei Hong Lim1Chun Kit Ang2https://orcid.org/0000-0002-1215-909XFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, MalaysiaThe electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the availability and reliability of the electrical generation in the power industry. Therefore, extracting and diagnosing the fault features from the process signals are useful to diagnose the status of the machine. Though, a common challenge in many applied applications is the practical knowledge about the risk of failure or historical records, which is totally unlabeled and difficult to be identified by traditional fault approaches. Hence, in the present study, a novel deep learning (DL) framework is proposed to fill the gap by balancing the three stages of fault feature extraction, fault detection, and parameter optimization based on the long short term memory- recurrent neural networks (RNN- LSTM), stacked autoencoders (SAE), and particle swarm optimization (PSO) techniques. The suggested framework focuses on failure detection through a sequence of numerous features for the unlabeled historical data and unknown anomaly. To validate the effectiveness of the proposed DL framework, an experiment for failure detection of the electrical generator was conducted for the data of risky environment at Yemen oil and gas plant. The experimental results compared with the earlier studies validate that, the DL framework can address the faults for vibration signals of the electrical generator in a well- diagnosis performance effectively.https://ieeexplore.ieee.org/document/9337852/Deep learning (DL)fault detectionlong short-term memory (LSTM)oil and gas plantrecurrent neural networks (RNN)stacked autoencoders (SAE) |
| spellingShingle | Moath Alrifaey Wei Hong Lim Chun Kit Ang A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator IEEE Access Deep learning (DL) fault detection long short-term memory (LSTM) oil and gas plant recurrent neural networks (RNN) stacked autoencoders (SAE) |
| title | A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator |
| title_full | A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator |
| title_fullStr | A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator |
| title_full_unstemmed | A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator |
| title_short | A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator |
| title_sort | novel deep learning framework based rnn sae for fault detection of electrical gas generator |
| topic | Deep learning (DL) fault detection long short-term memory (LSTM) oil and gas plant recurrent neural networks (RNN) stacked autoencoders (SAE) |
| url | https://ieeexplore.ieee.org/document/9337852/ |
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