Method and system for detecting sensor anomalies

Abstract

For detecting sensor anomalies, a machine learning model models a material flow in an industrial system, as a hierarchical time series, wherein the hierarchical time series represents a structure of the material flow using a directed acyclic graph with a set of nodes and a set of edges, wherein each node is associated to a time series, and wherein the edges represent parent-child relations where each value of a time series at a parent node equals the sum of the respective values of its child nodes. The machine learning model forecasts predicted time series values for all nodes. Current sensor measurements received from sensors placed in the industrial system are compared to the predictions of the machine learning model. An anomaly is detected if the difference exceeds a threshold.