基于多示例多标记的抽油机故障诊断

针对抽油机工况数据可从位移、载荷、电流等多个方面进行描述，若仅仅使用一个特征向量来描述抽油机工况数据会使其信息过于简化，丢失一部分有效信息的问题，以及工况数据具有多义性的特征，提出基于多示例多标记的抽油机故障诊断.该学习方法中，用抽油机的位移、载荷、电流数据作为抽油机工况样本包的多个示例，使用k-medoids聚类算法对样本包进行聚类，将多个样本包转换为若干示例，新示例的每一维表示样本包到样本各聚类中心的距离，再利用MLSVM算法对转换后的多标记问题进行求解.实验结果表明，多示例多标记学习能够及时、准确地诊断出抽油机故障问题.

Pumping Unit Diagnose Based on Muli-Instance and Multi-Label

The operating condition data of pumping unit can be described from the aspects of displacement,load and electric current.If only one feature vector is used to describe the operating condition of the pumping unit,the information will be too simplified,and it will lost some effective information.In view of the above problems and polysemy which is the essential characteristics of operating condition data,the fault diagnosis of pumping unit based on multi-instance and multi-label is presented.In this study,the displacement,load and current data of the pumping unit are used as multiple instances of pumping unit working condition data bags.Using k-medoids clustering algorithm cluster the bags and convert bags into several instances.Each dimension of the new instance indicates the distance from the bags to each cluster center,and then the MLSVM algorithm is used to solve the multi label problem.Experimental results show that multi-instance and multi-label learning can diagnose the trouble of oil pumping machine timely and accurately.