US 11,704,942 B2
Undercarriage wear prediction using machine learning model
Li Zhang, Dunlap, IL (US); Eric J. Johannsen, Washington, IL (US); Yanchai Zhang, Dunlap, IL (US); Xuefei Hu, Dunlap, IL (US); and Daniel W. Hoyt, Brimfield, IL (US)
Assigned to Caterpillar Inc., Peoria, IL (US)
Filed by Caterpillar Inc., Peoria, IL (US)
Filed on Oct. 29, 2020, as Appl. No. 16/949,448.
Prior Publication US 2022/0139117 A1, May 5, 2022
Int. Cl. G07C 5/00 (2006.01); G06N 20/00 (2019.01); G07C 5/08 (2006.01)
CPC G07C 5/006 (2013.01) [G06N 20/00 (2019.01); G07C 5/0808 (2013.01); G07C 5/0816 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method performed by a first device, the method comprising:
receiving, from one or more second devices, historical sensor data associated with wear of one or more components of an undercarriage of a machine,
wherein the historical sensor data includes machine vibration data regarding a measure of vibration of the machine;
receiving, from one or more third devices, historical inspection data associated with the wear of the one or more components;
training, using the historical sensor data and the historical inspection data, a machine learning model to predict a remaining life of the one or more components;
receiving, from one or more sensor devices of the machine, sensor data associated with the wear of the one or more components;
predicting, using the machine learning model and based on the sensor data, the remaining life of the one or more components; and
causing an action to be performed based on the remaining life of the one or more components.