US 12,146,570 B2
Dynamic belt tension inference method and related motor driven roller system
Ning Ting, Taoyuan (TW); and Chia-Hsien Chao, Taoyuan (TW)
Assigned to DELTA ELECTRONICS, INC., Taoyuan (TW)
Filed by DELTA ELECTRONICS, INC., Taoyuan (TW)
Filed on Jul. 18, 2023, as Appl. No. 18/223,524.
Claims priority of application No. 202310318297.5 (CN), filed on Mar. 29, 2023.
Prior Publication US 2024/0328484 A1, Oct. 3, 2024
Int. Cl. F16H 61/00 (2006.01); F16H 7/08 (2006.01); G05B 13/04 (2006.01)
CPC F16H 7/08 (2013.01) [G05B 13/042 (2013.01); F16H 2061/0093 (2013.01); F16H 2061/0096 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A dynamic belt tension inference method, comprising steps of:
(A) performing a training process, comprising steps of:
(A1) by a controller, using an Isolation Forest algorithm to train a tension inference model, wherein the tension inference model is configured to generate an anomaly score and a dynamic tension corresponding to the anomaly score;
(A2) by the controller, using the Isolation Forest algorithm under same hyper-parameter set to perform multiple trainings to generate multiple tension inference models;
(A3) by the controller, respectively computing multiple model performances of the multiple tension inference models according to the anomaly score and a pre-recorded data label;
(A4) by the controller, computing an averaged model performance of the multiple model performances;
(A5) by the controller, determining whether multiple averaged model performances have been acquired, wherein the multiple averaged model performances correspond to the multiple hyper-parameter sets;
(A6) by the controller, selecting one of the multiple hyper-parameter sets that corresponds to an optimal averaged model performance as a final hyper-parameter set for training to output a final model, wherein the final model includes a framework and parameters; and
(B) performing an inference process, comprising steps of:
(B1) by the controller, using the final model to infer processed data to generate the anomaly score and the dynamic tension corresponding to the anomaly score, wherein the processed data is generated by preprocessing data of a motor that drives a belt to rotate, and the final model is used to infer the dynamic tension of the belt while the belt is rotating.