首页> 外国专利> Machine learning of physical situations based on abstract relationships and sparse labels

Machine learning of physical situations based on abstract relationships and sparse labels

机译:基于抽象关系和稀疏标签的物理情况机器学习

摘要

A method for determining specific conditions occurring on industrial equipment based upon received signal data from sensors attached to the industrial equipment is provided. Using a server computer system, signal data is received and aggregated into feature vectors. Feature vectors represent a set of signal data over a particular range of time. The feature vectors are clustered into subsets of feature vectors based upon attributes the feature vectors. One or more sample episodes are received, where a sample episode includes sample feature vectors and specific classification labels assigned to the sample feature vectors. A signal data model is created that includes the associated feature vectors, clusters, and assigned classification labels. The signal data model is used to assign classification labels to newly received signal data using the mapping information for the existing feature vectors, existing clusters and associated classification labels to determine the specific conditions occurring on the industrial equipment.
机译:提供了一种用于基于来自附接到工业设备的传感器的接收到的信号数据来确定在工业设备上发生的特定条件的方法。使用服务器计算机系统,信号数据被接收并聚集为特征向量。特征向量表示特定时间范围内的一组信号数据。基于特征向量的属性,将特征向量聚类为特征向量的子集。接收一个或多个样本情节,其中样本情节包括样本特征向量和分配给样本特征向量的特定分类标签。创建一个信号数据模型,其中包括关联的特征向量,聚类和分配的分类标签。信号数据模型用于使用现有特征向量,现有群集和关联的分类标签的映射信息将分类标签分配给新接收的信号数据,以确定工业设备上发生的特定条件。

著录项

相似文献

  • 专利
  • 外文文献
  • 中文文献
获取专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号