US 11,747,191 B2
Automated health state classifier for rotating machines based on expert knowledge
Tobias Becker, Fürth (DE); Jonas Deichmann, Cologne (DE); Eugen Graz, Erlangen (DE); Henning Ochsenfeld, Nuremberg (DE); Thorsten Reimann, Erlangen (DE); and Jürgen Zettner, Veitsbronn (DE)
Assigned to Siemens Aktiengesellschaft, Munich (DE)
Appl. No. 17/426,386
Filed by Siemens Aktiengesellschaft, Munich (DE)
PCT Filed Feb. 17, 2020, PCT No. PCT/EP2020/054002
§ 371(c)(1), (2) Date Jul. 28, 2021,
PCT Pub. No. WO2020/182412, PCT Pub. Date Sep. 17, 2020.
Claims priority of application No. 19162895 (EP), filed on Mar. 14, 2019.
Prior Publication US 2022/0178737 A1, Jun. 9, 2022
Int. Cl. G01H 1/00 (2006.01); G01M 13/028 (2019.01); G06N 3/045 (2023.01)
CPC G01H 1/003 (2013.01) [G01M 13/028 (2013.01); G06N 3/045 (2023.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method of indicating machine failures, the method comprising:
receiving at least one input signal based on at least one physical quantity of at least one machine part of a machine, monitored by at least one sensor;
transforming the at least one input signal into at least one feature having a different domain and/or a different reference value than the at least one input signal, wherein the at least one input signal is transformed into a spectrum of the at least one input signal and into stochastic features for predominant parts of the spectrum; and
predicting a class and/or a severity of at least one machine failure based on the at least one feature by a neural network trained on specific patterns of the at least one feature or combinations of patterns of more than one feature indicative of specific machine failures.