论文标题
使用支持向量机对故障的无监督实时预测
Unsupervised Real Time Prediction of Faults Using the Support Vector Machine
论文作者
论文摘要
本文旨在提高使用顺序最小优化（SMO）培训算法的支撑矢量机（SVM）分类器的分类准确性，以便对石油和天然气设备数据的正常实例进行适当的分类。失败分析的最新应用在不实施SMO培训算法的情况下使用了SVM技术，而在我们的研究中，我们表明，使用SMO培训算法时，提出的解决方案可以表现更好。此外，我们实施了集合方法，这是一种基于混合规则的神经网络分类器，可提高SVM分类器的性能（具有SMO培训算法）。优化研究是分类器在处理不平衡数据集时表现不佳的结果。通过使用堆叠集合方法，将选定的最佳性能分类器与SVM分类器（带有SMO培训算法）结合在一起，该方法是创建有效的集合预测模型，该模型可以处理不平衡数据的问题。这种预测模型的分类性能要比具有和没有SMO培训算法的SVM和许多其他常规分类器要好得多。
This paper aims at improving the classification accuracy of a Support Vector Machine (SVM) classifier with Sequential Minimal Optimization (SMO) training algorithm in order to properly classify failure and normal instances from oil and gas equipment data. Recent applications of failure analysis have made use of the SVM technique without implementing SMO training algorithm, while in our study we show that the proposed solution can perform much better when using the SMO training algorithm. Furthermore, we implement the ensemble approach, which is a hybrid rule based and neural network classifier to improve the performance of the SVM classifier (with SMO training algorithm). The optimization study is as a result of the underperformance of the classifier when dealing with imbalanced dataset. The selected best performing classifiers are combined together with SVM classifier (with SMO training algorithm) by using the stacking ensemble method which is to create an efficient ensemble predictive model that can handle the issue of imbalanced data. The classification performance of this predictive model is considerably better than the SVM with and without SMO training algorithm and many other conventional classifiers.
