Abstract: A method for part replacement timing optimization. The method includes training a wear estimate model. Training the model includes predicting a plurality of wear patterns for a part, each wear pattern corresponding to a degree of severity. Training images are rendered for each wear pattern. Each of the training images is labeled with the corresponding degree of severity. A neural network is then trained with the labeled training images. An image of a deployed part associated with a machine is received and fed into the trained wear estimate model. The method further includes receiving a wear estimate for the part image from the trained wear estimate model, estimating a change in performance of the machine based on the wear estimate, and determining a machine utilization pattern for the machine. The machine utilization pattern and the change in performance estimate are combined to determine an optimal time to replace the part.

Abstract: A method for part replacement timing optimization. The method includes training a wear estimate model. Training the model includes predicting a plurality of wear patterns for a part, each wear pattern corresponding to a degree of severity. Training images are rendered for each wear pattern. Each of the training images is labeled with the corresponding degree of severity. A neural network is then trained with the labeled training images. An image of a deployed part associated with a machine is received and fed into the trained wear estimate model. The method further includes receiving a wear estimate for the part image from the trained wear estimate model, estimating a change in performance of the machine based on the wear estimate, and determining a machine utilization pattern for the machine. The machine utilization pattern and the change in performance estimate are combined to determine an optimal time to replace the part.