| 基于数据驱动方法的集装箱龙门起重机能源系统健康状态预测 |
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| 引用本文： | 杜明泽,嘉红霞.基于数据驱动方法的集装箱龙门起重机能源系统健康状态预测[J].上海海事大学学报,2018,39(4):70-74. |
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| 作者姓名： | 杜明泽 嘉红霞 |
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| 作者单位： | 上海海事大学物流工程学院,上海海事大学物流工程学院 |
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| 摘 要： | 为实现起重机节能环保和延长电池组的寿命,以电池的剩余容量作为集装箱龙门起重机能源系统健康状态的评价标准,建立BP神经网络和最小二乘支持向量机(least squares support vector machine,LSSVM)两种电池剩余容量预测模型。分别采用梯度下降算法和标准粒子群优化算法对两种预测模型中的参数进行优化。利用训练好的模型进行电池剩余容量预测。将两种模型的预测值与实测值进行对比分析,结果表明这两种模型都具有高的预测精度,而LSSVM模型是更合适的预测模型。
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| 关 键 词： | 集装箱龙门起重机 能源系统 健康状态预测 BP神经网络 最小二乘支持向量机(LSSVM） |
| 收稿时间： | 2017/8/20 0:00:00 |
| 修稿时间： | 2017/9/13 0:00:00 |
Health prediction of energy system of gantry container cranes based on data driven method |
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| Institution: | Logistics Engineering College of SMU |
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| Abstract: | In order to realize the energy saving and environmental protection of cranes and prolong the life of battery pack, the remaining battery capacity is used as the evaluation standard for the health of the energy system of gantry container cranes. Two remaining capacity prediction models of BP neural network and the least squares support vector machine (LSSVM) are established. The parameters of the two prediction models are optimized by the gradient descent algorithm and the standard particle swarm optimization algorithm. The remaining battery capacity is predicted by the trained models. Comparing the predicted values of the two models with the measured values, it shows that the two models are both of high prediction accuracy, and the LSSVM model is more appropriate. |
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| Keywords: | gantry container crane energy system health prediction BP neural network least squares support vector machine (LSSVM) |
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