US 11,941,521 B2
Vibrating machine automated diagnosis with supervised learning
Bertrand Wascat, Limonest (FR); Philippe Poizat, Limonest (FR); and Jean Michel Becu, Limonest (FR)
Assigned to ACOEM France, Limonest (FR)
Filed by ACOEM France, Limonest (FR)
Filed on Sep. 11, 2020, as Appl. No. 17/017,912.
Prior Publication US 2022/0083851 A1, Mar. 17, 2022
Int. Cl. G06N 3/08 (2023.01); G05B 23/02 (2006.01); G06N 3/047 (2023.01)
CPC G06N 3/08 (2013.01) [G05B 23/0221 (2013.01); G05B 23/0272 (2013.01); G05B 23/0283 (2013.01); G06N 3/047 (2023.01)] 4 Claims
OG exemplary drawing
 
1. A method with learning for generating an overall diagnostic health rating for a machine having moving parts, comprising the steps:
sensing machine diagnostic data, including vibration data, using one or more sensors installed at the machine;
deriving with one or more first processors a prescribed set of indicators from the machine diagnostic data;
for a first set of said machine diagnostic data, automatically diagnosing by a monitoring unit processor a first overall diagnostic health rating of the machine from the first set of said machine diagnostic data and a subset of the prescribed set of indicators, wherein said automatic diagnosing comprises said monitoring unit processor implementing a neural network classification model configured with a set of parameters defined for a prescribed set of symptoms for a prescribed set of diagnostic conditions monitored at the machine;
in response to a review of the prescribed set of indicators and the first set of said machine diagnostic data, storing in memory a corrected overall diagnostic health rating of the machine which differs from said first overall diagnostic health rating of the machine;
modifying with a third processor the neural network classification model so as to generate said corrected overall diagnostic health rating in response to said first set of machine diagnostic data, wherein said learning comprises said modifying step;
downloading the modified neural network classification model to a monitoring unit comprising said monitoring unit processor;
repeating said steps of sensing and deriving to derive a newly-derived set of indicators from newly-collected machine diagnostic data; and
based on the newly-derived set of indicators, automatically diagnosing by said monitoring unit processor a second overall diagnostic health rating of the machine using the modified neural network classification model.