| 基于SVM模型算法和大数据分析技术的船舶设备故障诊断 |
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| 作者姓名： | 王晓东 马旭颖 |
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| 作者单位： | 上海船舶运输科学研究所舰船自动化系统事业部 |
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| 摘 要： | 为在船舶设备发生故障时能准确、及时地定位故障发生根源,保证船舶安全、经济运行,采用大数据分析方法和支持向量机(Support Vector Machine,SVM)模型算法对船舶设备进行故障诊断,提前预测可能发生的故障。以船舶柴油机滑油压力低故障为例,应用Python语言,通过SVM模型算法预测该故障的发生概率。结果表明,在已采集的船舶数据样本的训练集和测试集上,数据拟合和故障预测的效果十分理想,预测故障发生的准确率较高。
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| 关 键 词： | 大数据分析 支持向量机模型算法 PYTHON语言 船舶设备故障诊断 |
Big Data Analysis with SVM Model Algorithm for Fault Diagnosis of Marine Equipment |
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| Authors: | WANG Xiaodong MA Xuying |
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| Institution: | (Warship Automatic System Division,Shanghai Ship and Shipping Research Institute,Shanghai 200135,China) |
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| Abstract: | The SVM(Support Vector Machine)model algorithm for predicting the probability of low lubrication oil pressure in a diesel engine is developed with Python.The model is trained with a training data set and verified with a test data set.Satisfactory data fitting and fault prediction are demonstrated. |
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| Keywords: | big data analysis SVM(Support Vector Machine)model algorithm Python fault diagnosis of marine equipment |
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