Fault detection and diagnosis for process control rig using artificial intelligent
This paper focuses on the application of artificial intelligent techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a process control rig. Fuzzy logic with genetic algorithm method is used to develop fault model and to detect the fault whe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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ICIC International
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/14736/1/Fault%20detection%20and%20diagnosis%20for%20process%20control%20rig%20using%20artificial%20intelligent.pdf |
| _version_ | 1796968705175846912 |
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| author | Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki |
| author_facet | Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki |
| author_sort | Yusof, Rubiyah |
| collection | UPM |
| description | This paper focuses on the application of artificial intelligent techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a process control rig. Fuzzy logic with genetic algorithm method is used to develop fault model and to detect the fault where this task is performed by using the error signals, where when error signal is zero or nearly zero, the system is in normal condition, and when the fault occurs, error signals should distinctively diverge from zero. Meanwhile, neural network is used for fault classification where this task is performed by identifying the fault in the system. |
| first_indexed | 2024-03-06T07:32:15Z |
| format | Article |
| id | upm.eprints-14736 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:32:15Z |
| publishDate | 2010 |
| publisher | ICIC International |
| record_format | dspace |
| spelling | upm.eprints-147362015-10-30T00:37:18Z http://psasir.upm.edu.my/id/eprint/14736/ Fault detection and diagnosis for process control rig using artificial intelligent Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki This paper focuses on the application of artificial intelligent techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a process control rig. Fuzzy logic with genetic algorithm method is used to develop fault model and to detect the fault where this task is performed by using the error signals, where when error signal is zero or nearly zero, the system is in normal condition, and when the fault occurs, error signals should distinctively diverge from zero. Meanwhile, neural network is used for fault classification where this task is performed by identifying the fault in the system. ICIC International 2010-10 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/14736/1/Fault%20detection%20and%20diagnosis%20for%20process%20control%20rig%20using%20artificial%20intelligent.pdf Yusof, Rubiyah and Abdul Rahman, Ribhan Zafira and Khalid, Marzuki (2010) Fault detection and diagnosis for process control rig using artificial intelligent. ICIC Express Letters, 4 (5B). pp. 1811-1816. ISSN 1881-803X |
| spellingShingle | Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Fault detection and diagnosis for process control rig using artificial intelligent |
| title | Fault detection and diagnosis for process control rig using artificial intelligent |
| title_full | Fault detection and diagnosis for process control rig using artificial intelligent |
| title_fullStr | Fault detection and diagnosis for process control rig using artificial intelligent |
| title_full_unstemmed | Fault detection and diagnosis for process control rig using artificial intelligent |
| title_short | Fault detection and diagnosis for process control rig using artificial intelligent |
| title_sort | fault detection and diagnosis for process control rig using artificial intelligent |
| url | http://psasir.upm.edu.my/id/eprint/14736/1/Fault%20detection%20and%20diagnosis%20for%20process%20control%20rig%20using%20artificial%20intelligent.pdf |
| work_keys_str_mv | AT yusofrubiyah faultdetectionanddiagnosisforprocesscontrolrigusingartificialintelligent AT abdulrahmanribhanzafira faultdetectionanddiagnosisforprocesscontrolrigusingartificialintelligent AT khalidmarzuki faultdetectionanddiagnosisforprocesscontrolrigusingartificialintelligent |