A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector
In production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Marko...
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| Format: | Article |
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MDPI AG
2020-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6902 |
| _version_ | 1827705052232417280 |
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| author | Nan Pang Peng Jia Peilin Liu Feng Yin Lei Zhou Liquan Wang Feihong Yun Xiangyu Wang |
| author_facet | Nan Pang Peng Jia Peilin Liu Feng Yin Lei Zhou Liquan Wang Feihong Yun Xiangyu Wang |
| author_sort | Nan Pang |
| collection | DOAJ |
| description | In production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Markov method, this paper proposed a fuzzy Markov method that takes the full advantages of the three methods and makes the analysis of risk, reliability, availability and uncertainty all in one. This method uses the fault tree and fuzzy theory to preprocess the input failure data to improve the reliability of the input failure data, and then input the preprocessed failure data into the Markov model; after that iterate and adjust the model when uncertainty events occur, until the data of all events have been processed by the model and the updated model obtained, which best reflects the system state. The wellhead connector of a subsea production system was used as a case study to demonstrate the above method. The obtained reliability index (mean time to failure) of the connector is basically consistent with the failure statistical data from the offshore and onshore reliability database, which verified the accuracy of the proposed method. |
| first_indexed | 2024-03-10T15:53:54Z |
| format | Article |
| id | doaj.art-63f0eef3e82c4fd79988f80f5d949964 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:53:54Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-63f0eef3e82c4fd79988f80f5d9499642023-11-20T15:49:33ZengMDPI AGApplied Sciences2076-34172020-10-011019690210.3390/app10196902A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead ConnectorNan Pang0Peng Jia1Peilin Liu2Feng Yin3Lei Zhou4Liquan Wang5Feihong Yun6Xiangyu Wang7College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaOffshore Oil Engineering CO. LTD, Tianjin 300450, ChinaCNOOC Research Institute CO. LTD, Beijing, 100028, ChinaOffshore Oil Engineering CO. LTD, Tianjin 300450, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaIn production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Markov method, this paper proposed a fuzzy Markov method that takes the full advantages of the three methods and makes the analysis of risk, reliability, availability and uncertainty all in one. This method uses the fault tree and fuzzy theory to preprocess the input failure data to improve the reliability of the input failure data, and then input the preprocessed failure data into the Markov model; after that iterate and adjust the model when uncertainty events occur, until the data of all events have been processed by the model and the updated model obtained, which best reflects the system state. The wellhead connector of a subsea production system was used as a case study to demonstrate the above method. The obtained reliability index (mean time to failure) of the connector is basically consistent with the failure statistical data from the offshore and onshore reliability database, which verified the accuracy of the proposed method.https://www.mdpi.com/2076-3417/10/19/6902fuzzy comprehensive evaluationfuzzy Markovavailability analysisreliability indexuncertaintywellhead connector |
| spellingShingle | Nan Pang Peng Jia Peilin Liu Feng Yin Lei Zhou Liquan Wang Feihong Yun Xiangyu Wang A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector Applied Sciences fuzzy comprehensive evaluation fuzzy Markov availability analysis reliability index uncertainty wellhead connector |
| title | A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector |
| title_full | A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector |
| title_fullStr | A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector |
| title_full_unstemmed | A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector |
| title_short | A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector |
| title_sort | fuzzy markov model for risk and reliability prediction of engineering systems a case study of a subsea wellhead connector |
| topic | fuzzy comprehensive evaluation fuzzy Markov availability analysis reliability index uncertainty wellhead connector |
| url | https://www.mdpi.com/2076-3417/10/19/6902 |
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