Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network
The landing phase of an airdrop process is prone to accidents, and thus, it is important to assess the landing reliability for an airdrop system. However, full field tests to assess the reliability are unacceptable due to their cost and the time required. As such, it is necessary to estimate the rel...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/1773841 |
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| author | Wei Cheng Chunxin Yang Peng Ke |
| author_facet | Wei Cheng Chunxin Yang Peng Ke |
| author_sort | Wei Cheng |
| collection | DOAJ |
| description | The landing phase of an airdrop process is prone to accidents, and thus, it is important to assess the landing reliability for an airdrop system. However, full field tests to assess the reliability are unacceptable due to their cost and the time required. As such, it is necessary to estimate the reliability in the design stage. To address this problem, a method based on vine-Bayesian Network (vine-BN) is proposed to assess the landing reliability by fusing multisource information. First, the network structure is determined by the relationship between data of simulation or ground tests and failure modes. Then, nodes are defined as random variables on [0, 1] based on the definition of the performance metric. Finally, the dependence between nodes is quantified by expert opinions. To illustrate the effectiveness of the method, a particular ground test or simulation is chosen to establish a network for a typical heavy cargo airdrop system (HCADS). Forward and backward propagation is carried out on the network. The forward analysis predicts the landing reliability in the design stage through multisource information fusion. Beta distribution is applied to fit the fusion result, so Bayesian inference is made to perform field test times decision-making. The backward analysis works to identify the key performance metrics related to landing reliability. The results and analysis manifest that vine-BN is feasible for fusing multisource information. Through the network, the reliability of the current design can be predicted effectively, and the field test times can be remarkably reduced. This method plays a crucial role in airdrop system design and reducing test time and labor. |
| format | Article |
| id | doaj-art-a5d2bc74b29c436eb914ff568aa14803 |
| institution | Kabale University |
| issn | 1687-5974 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-a5d2bc74b29c436eb914ff568aa148032025-08-20T03:38:44ZengWileyInternational Journal of Aerospace Engineering1687-59742023-01-01202310.1155/2023/1773841Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian NetworkWei Cheng0Chunxin Yang1Peng Ke2School of Aeronautical Science and EngineeringSchool of Aeronautical Science and EngineeringSchool of Transportation Science and EngineeringThe landing phase of an airdrop process is prone to accidents, and thus, it is important to assess the landing reliability for an airdrop system. However, full field tests to assess the reliability are unacceptable due to their cost and the time required. As such, it is necessary to estimate the reliability in the design stage. To address this problem, a method based on vine-Bayesian Network (vine-BN) is proposed to assess the landing reliability by fusing multisource information. First, the network structure is determined by the relationship between data of simulation or ground tests and failure modes. Then, nodes are defined as random variables on [0, 1] based on the definition of the performance metric. Finally, the dependence between nodes is quantified by expert opinions. To illustrate the effectiveness of the method, a particular ground test or simulation is chosen to establish a network for a typical heavy cargo airdrop system (HCADS). Forward and backward propagation is carried out on the network. The forward analysis predicts the landing reliability in the design stage through multisource information fusion. Beta distribution is applied to fit the fusion result, so Bayesian inference is made to perform field test times decision-making. The backward analysis works to identify the key performance metrics related to landing reliability. The results and analysis manifest that vine-BN is feasible for fusing multisource information. Through the network, the reliability of the current design can be predicted effectively, and the field test times can be remarkably reduced. This method plays a crucial role in airdrop system design and reducing test time and labor.http://dx.doi.org/10.1155/2023/1773841 |
| spellingShingle | Wei Cheng Chunxin Yang Peng Ke Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network International Journal of Aerospace Engineering |
| title | Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network |
| title_full | Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network |
| title_fullStr | Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network |
| title_full_unstemmed | Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network |
| title_short | Landing Reliability Assessment of Airdrop System Based on Vine-Bayesian Network |
| title_sort | landing reliability assessment of airdrop system based on vine bayesian network |
| url | http://dx.doi.org/10.1155/2023/1773841 |
| work_keys_str_mv | AT weicheng landingreliabilityassessmentofairdropsystembasedonvinebayesiannetwork AT chunxinyang landingreliabilityassessmentofairdropsystembasedonvinebayesiannetwork AT pengke landingreliabilityassessmentofairdropsystembasedonvinebayesiannetwork |