Caterpillar Inc.

Daniel Joseph Reaume

Kyle J Cline

Daniel Jude Organ

Michael E Sharov

Daniela Radakovic

Melissa A Busen

Systems and methods for scheduling maintenance of equipment include collecting pre-emptive maintenance data associated with a pre-emptive maintenance task associated with a component of equipment, collecting preventive maintenance data associated with a scheduled preventive maintenance task associated with the component or another component, applying a model to predict a failure time value associated with a future failure of the component, analyzing the data and the time value to determine an optimal time to perform a maintenance event, and using that optimal time to schedule the next maintenance event. Determining the optimal time includes minimizing a total cost of the maintenance.

Systems and methods for automatically scheduling maintenance

Systems and methods for improving recordkeeping and analysis of an asset include creating and maintaining an integrated record about the asset. In some embodiments, the systems and methods include collecting data about an asset to form an asset data collection, recording the asset data collection in a record, analyzing at least a portion of the asset data collection to determine a characteristic of the asset, and recording the characteristic of the asset in the record. In some embodiments, recording the characteristic in the record includes adding the characteristic to the asset data collection.

Integrated record of asset usage, maintenance, and condition, and associated systems and methods

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.

Systems and methods for wear assessment and part replacement timing optimization

Systems and methods for monitoring an operator of an asset are described herein. The method includes receiving training data, the training data comprising training sensor data associated with one or more tasks performed by a plurality of operators of different skill levels and under different performance impairments. The method can also include training a machine learning model to recognize one or more operator conditions based on the received training data and receiving sensor data from a plurality of sensors associated with the asset or the operator. The method can further include determining an operator condition of the operator based on the received sensor data and the machine learning model and taking one or more actions in response to the determined operator condition.

Monitoring operator condition using sensor data

A method for object identification using multiple images. The method includes training an object identification model. Training the model includes collecting a training images for each of a plurality of objects, labeling each of the plurality of training images with a corresponding one of a plurality of object identifiers, and training a neural network with the plurality of labeled training images. At least two target images of a target object are receive and fed into the trained object identification model. The method further includes receiving, from the trained object identification model, for each of the at least two target images, an object identifier corresponding to the target object and a probability that the object identifier corresponds to the target object. A similarity value between the at least two target images is computed and the probabilities for the at least two target images are combined in proportion to the similarity value.

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Midland, MI, United States of America

Daniel Joseph Reaume