| CPC G05B 19/4155 (2013.01) [G06N 20/00 (2019.01); G06V 10/70 (2022.01); G05B 2219/31368 (2013.01)] | 13 Claims |
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1. A method, comprising:
extracting features from each of a plurality of time-series sensor data, the plurality of time-series sensor data associated with execution of one or more operations;
clustering the extracted features into a plurality of tasks that occur from execution of the one or more operations, each of the plurality of tasks associated with a clustering identifier (ID) from the clustering;
calculating a cycle time of the cycle based on the initiation and end of the cycle recognized by referencing a cycle pattern model, wherein the cycle pattern model comprises configuration information of a cycle comprising a set from a plurality of the clustering IDs; and
learning the cycle pattern model, the learning the cycle pattern model comprising:
extracting features from each of a plurality of other time-series sensor data, the plurality of other time-series sensor data associated with execution of one or more operations,
clustering the extracted features into a plurality of clusters, each of the cluster associated with a clustering identifier (ID) from the clustering,
executing frequent pattern extraction on the clustering IDs,
associating production operation information received in time series to the clustered features,
determining initiation timing that meets a requirement from the production operation information, and
determining the clustering ID from the plurality of clustering IDs which corresponds to the initiation of the cycle.
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