Industrial robot production data clustering for anomaly prediction

Abstract

A method and system for analyzing robot data to identify anomalies. Data from robot production operations are provided to a processor running a K-means clustering algorithm which separates the data into a number (K) of clusters. The K-means clustering algorithm is executed two or more times on the data, each time using a different value of K within a predefined range. A scoring technique is used to determine an optimal value for the number of clusters K, where the score is calculated in a computation which rewards small distances between points within a cluster and large distances between points in different clusters. The K-means clustering results for the optimal value of K are used to separate the data by cluster, whereupon the separated data is analyzed to identify trends in the parameter data. One of the clusters may contain outlier data points, some of which may indicate an anomaly condition.