US 12,321,159 B2
Systems, methods, and media for monitoring the production process of a product
Chris Peter Tsokos, Tampa, FL (US); and Lohuwa Mamudu, Tampa, FL (US)
Assigned to UNIVERSITY OF SOUTH FLORIDA, Tampa, FL (US)
Filed by University of South Florida, Tampa, FL (US)
Filed on Feb. 4, 2022, as Appl. No. 17/665,296.
Prior Publication US 2023/0251640 A1, Aug. 10, 2023
Int. Cl. G05B 19/418 (2006.01)
CPC G05B 19/4188 (2013.01) [G05B 19/4183 (2013.01); G05B 19/41875 (2013.01); G05B 19/41885 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A system for monitoring a production process of a product, the system comprising:
a remote server;
a communications connection between the remote server and a facility database;
at least one processor coupled to the communications connection; and
a memory device having stored thereon a set of computer readable instructions which, when executed by the at least one processor, cause the at least one processor to:
receive a first set of data related to the production process;
calculate a first monitoring index indicator for the production process based on the first set of data;
receive a second set of data related to the production process, after one or more performance variables of the production process are modified;
calculate a second monitoring index indicator for the production process based on the second set of data; and
output a result based on the first and second monitoring index indicators;
wherein the result indicates an impact that the one or more modified performance variables had on the production process;
wherein the processor is further configured, in coordination with the memory device, to:
calculate an intensity value for each monitoring index indicator that depends on a performance variable;
determine the impact that each performance variable has on the production process based on the intensity values;
generate a trained model based on the intensity values and corresponding impacts; and
increase an output of the production process by modifying at least one performance variable based on the trained model.