EVALUATION FOR REMAINING LIFE OF BRIDGE CRANE BASED ON RADIAL BASIS NEURAL NETWORK
In order to reduce the probability of crane safety accidents, this paper proposes a method to quickly calculate the remaining life of the crane based on radial basis function（RBF）neural network. Taking a bridge crane in a factory as an example, an ANSYS finite element model is established based on a...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2021-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.025 |
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| _version_ | 1841535978034954240 |
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| author | ZUO Yang YANG RongPing MA HaoQin QIN Ze BAO DongJie |
| author_facet | ZUO Yang YANG RongPing MA HaoQin QIN Ze BAO DongJie |
| author_sort | ZUO Yang |
| collection | DOAJ |
| description | In order to reduce the probability of crane safety accidents, this paper proposes a method to quickly calculate the remaining life of the crane based on radial basis function（RBF）neural network. Taking a bridge crane in a factory as an example, an ANSYS finite element model is established based on actual parameters, and the model is modified through on-site measured data, and a static analysis is performed to obtain the location of the fatigue calculation point. Firstly, taking position of the trolley and the lifting load as input layer, the equivalent stress value at any point as output layer to stimulate the typical working conditions of crane operation. Secondly, to obtain time stress curve at any point quickly by using the well-trained RBF neural network model. Finally, to evaluate the residual life according to the damage tolerance fracture mechanics method. The results show that the time stress curve can be quickly obtained from any node by using the radial basis neural network model, which greatly decreased cumbersome process and save the cost in the crane site measurement, and realize the fast acquisition of the time stress curve to calculate the fatigue remaining life. Completing the estimation of the remaining fatigue life of the bridge crane provides a reliable basis for the long-term safe use and later maintenance of the crane. |
| format | Article |
| id | doaj-art-d53bcbc48cbf4d95943832deb45496fe |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-d53bcbc48cbf4d95943832deb45496fe2025-01-15T02:25:07ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-01431450145530612357EVALUATION FOR REMAINING LIFE OF BRIDGE CRANE BASED ON RADIAL BASIS NEURAL NETWORKZUO YangYANG RongPingMA HaoQinQIN ZeBAO DongJieIn order to reduce the probability of crane safety accidents, this paper proposes a method to quickly calculate the remaining life of the crane based on radial basis function（RBF）neural network. Taking a bridge crane in a factory as an example, an ANSYS finite element model is established based on actual parameters, and the model is modified through on-site measured data, and a static analysis is performed to obtain the location of the fatigue calculation point. Firstly, taking position of the trolley and the lifting load as input layer, the equivalent stress value at any point as output layer to stimulate the typical working conditions of crane operation. Secondly, to obtain time stress curve at any point quickly by using the well-trained RBF neural network model. Finally, to evaluate the residual life according to the damage tolerance fracture mechanics method. The results show that the time stress curve can be quickly obtained from any node by using the radial basis neural network model, which greatly decreased cumbersome process and save the cost in the crane site measurement, and realize the fast acquisition of the time stress curve to calculate the fatigue remaining life. Completing the estimation of the remaining fatigue life of the bridge crane provides a reliable basis for the long-term safe use and later maintenance of the crane.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.025Bridge craneFinite elementNeural NetworksRemaining life |
| spellingShingle | ZUO Yang YANG RongPing MA HaoQin QIN Ze BAO DongJie EVALUATION FOR REMAINING LIFE OF BRIDGE CRANE BASED ON RADIAL BASIS NEURAL NETWORK Jixie qiangdu Bridge crane Finite element Neural Networks Remaining life |
| title | EVALUATION FOR REMAINING LIFE OF BRIDGE CRANE BASED ON RADIAL BASIS NEURAL NETWORK |
| title_full | EVALUATION FOR REMAINING LIFE OF BRIDGE CRANE BASED ON RADIAL BASIS NEURAL NETWORK |
| title_fullStr | EVALUATION FOR REMAINING LIFE OF BRIDGE CRANE BASED ON RADIAL BASIS NEURAL NETWORK |
| title_full_unstemmed | EVALUATION FOR REMAINING LIFE OF BRIDGE CRANE BASED ON RADIAL BASIS NEURAL NETWORK |
| title_short | EVALUATION FOR REMAINING LIFE OF BRIDGE CRANE BASED ON RADIAL BASIS NEURAL NETWORK |
| title_sort | evaluation for remaining life of bridge crane based on radial basis neural network |
| topic | Bridge crane Finite element Neural Networks Remaining life |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.025 |
| work_keys_str_mv | AT zuoyang evaluationforremaininglifeofbridgecranebasedonradialbasisneuralnetwork AT yangrongping evaluationforremaininglifeofbridgecranebasedonradialbasisneuralnetwork AT mahaoqin evaluationforremaininglifeofbridgecranebasedonradialbasisneuralnetwork AT qinze evaluationforremaininglifeofbridgecranebasedonradialbasisneuralnetwork AT baodongjie evaluationforremaininglifeofbridgecranebasedonradialbasisneuralnetwork |