US 12,437,234 B2
System and method for optimal drive configuration using machine learning
Pablo Rodriguez, Ilvesheim (DE); Prerna Juhlin, Heidelberg (DE); Jens Doppelhamer, Ladenburg (DE); Katharina Stark, Weinheim (DE); and Somayeh Malakuti, Dossenheim (DE)
Assigned to ABB Schweiz AG, Baden (CH)
Filed by ABB Schweiz AG, Baden (CH)
Filed on Oct. 13, 2021, as Appl. No. 17/499,917.
Claims priority of application No. 20201731 (EP), filed on Oct. 14, 2020.
Prior Publication US 2022/0114493 A1, Apr. 14, 2022
Int. Cl. G06N 20/00 (2019.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/40 (2023.01); G06F 30/27 (2020.01)
CPC G06N 20/00 (2019.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/40 (2023.01); G06F 30/27 (2020.01)] 9 Claims
OG exemplary drawing
 
1. A system for optimal drive configuration using machine learning, the system comprising;
a non-transitory computer-readable medium comprising:
a data collector configured to collect data and establish correlations among the collected data, wherein the data collector is configured to collect the data by adopting text processing approaches or text mining approaches to extract features from customer documents and technical requirements of an industrial application associated with the drive;
a training data set generator configured to compute configuration sets for the drive based on the collected data and based on the established correlations, further configured to compute measured success values for the computed configuration sets, further configured to generate training data sets comprising the computed configuration sets together with corresponding measured success values;
a machine learning module, configured to predict predicted success values for the computed configuration sets using the training data sets provided by the training data set generator using a machine learning algorithm;
an optimization module, configured to order the computed configuration sets that has the predicted success values above a certain threshold, comprising a simulation module, configured to simulate the ordered configuration sets and evaluate results for convergence; and
a user interface module configured to present a graphical user interface with the computed configuration sets for which the results are converged, and initiate a feedback mechanism for a user to provide feedback regarding the presented configuration sets such that the feedback is used to tune the machine learning algorithm to achieve the optimal drive configuration.