Method of Hierarchical Machine Learning for an Industrial Plant Machine Learning System

Abstract

A method of hierarchical machine learning includes receiving a topology model having information on hierarchical relations between components of the industrial plant, determining a representation hierarchy comprising a plurality of levels, wherein each representation on a higher level represents a group of representations on a lower level, wherein the representations comprise a machine learning model, and training an output machine learning model using the determined hierarchical representations.

What is claimed is: 
     
         1 . A computer-implemented method of hierarchical machine learning for an industrial plant machine learning system, comprising:
 receiving, by a machine learning unit, a topology model comprising structural information on hierarchical relations between components of the industrial plant, wherein the components comprise data signals of sensors of the industrial plant and hierarchical units, wherein the hierarchical units comprise assets, plant sub-units, plant units and plant sections of the industrial plant;   determining, by the machine learning unit, a representation hierarchy comprising a plurality of levels using the received data signals and the received topology model, wherein the representation hierarchy comprises a signal representation for each of the plurality of received data signals and a hierarchical representation for each of the hierarchical units on different levels;   wherein each representation on a higher level represents a group of representations on a lower level;   wherein each of the signal representation and the hierarchical representation comprise a machine learning model;   training, by the machine learning unit, an output machine learning model of the machine learning unit using the determined hierarchical representations.   
     
     
         2 . The method of  claim 1 , wherein the representation hierarchy comprises at least a bottom level and at least a target level, wherein the bottom level comprises the signal representations, and wherein the target level comprises the hierarchical representations. 
     
     
         3 . The method of  claim 1 , wherein the representation hierarchy comprises at least an intermediate level, wherein the at least one intermediate level comprises the hierarchical representations with a lower level than the target level. 
     
     
         4 . The method of  claim 2 , wherein the target level comprises only one hierarchical representation that contains information about all lower level representations. 
     
     
         5 . The method of  claim 1 , wherein training the output machine learning model comprises training the output machine learning model using the hierarchical representations of the previous levels. 
     
     
         6 . The method of  claim 1 , wherein training the output machine learning model comprises training the output machine learning model using the hierarchical representations of the target level. 
     
     
         7 . The method of  claim 1 , wherein determining the representation hierarchy comprises learning, for each data signal, a signal representation and learning, for each hierarchical unit, a hierarchical representation, and wherein each hierarchical representation is learned based on corresponding representations of a previous level. 
     
     
         8 . The method of  claim 7 , wherein learning the signal representation and the hierarchical representation comprises using a dimensionality reduction method. 
     
     
         9 . The method of  claim 1 , wherein determining the representation hierarchy comprises:
 determining a distance matrix between the representations using the received topology model;   identifying hierarchical representations as parent representations and its corresponding children representations on a lower level using the determined distance matrix; and   learning the parent representations using the identified children representations.   
     
     
         10 . The method of  claim 9 , wherein learning the parent representations using the identified children representations comprises:
 determining reconstructed children data by decoding the identified children representations; and   learning the parent representations using the reconstructed children data.   
     
     
         11 . The method of  claim 10 , wherein decoding the identified children representations comprises reconstructing and/or de-noising the data of the identified children representations. 
     
     
         12 . The method of  claim 9 , further comprising repeating identifying hierarchical representations as parent representations and its corresponding children representations from a lower level to a higher level until the target level is reached. 
     
     
         13 . The method of  claim 1 , wherein the topology model and the data signals of sensors of the industrial plant are provided by the industrial plant and/or by an industrial plant simulation. 
     
     
         14 . The method of  claim 1 , wherein the topology model comprises structural information on hierarchical relations between process steps in a process recipe processed by the industrial plant.