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Machine learning of physical situations based on abstract relationships and sparse labels
Machine learning of physical situations based on abstract relationships and sparse labels
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机译:基于抽象关系和稀疏标签的物理情况机器学习
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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.
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