Caterpillar Inc.

Asiatrak (tianjin) Ltd.

Li Zhang

Eric James Johannsen

Yanchai Zhang

Xuefei Hu

Thomas J Yaniak

Daniel Wilson Hoyt

Donald John Kalmes

Yingbiao Xu

William P Shoemaker

Jichul Choi

Yeonbok Muk

Gilsung Kim

Yaodong Zhou

A system may include a first device and a second device. The first device may be configured to be associated with a component of a machine. The second device may be configured to provide one or more interrogation magnetic signals to the first device; and determine, based on providing the one or more interrogation magnetic signals, whether a response magnetic signal is received from the first device. The second device may selectively provide first wear information indicating a first amount of wear of the component or second wear information indicating a second amount of wear of the component. The first information may be provided when the response magnetic signal is received from the first device. The second wear information may be provided when the response magnetic signal is not received from the first device. The second amount of wear may exceed the first amount of wear.

Component monitoring based on magnetic signals

A track link having sensor-receiving cavities and a method of producing a track link is disclosed. The track link may include a link body and a cavity formed in the link body configured to receive a sensing device. The track link may also include a protrusion substantially surrounding the cavity. The protrusion may include a substantially flat surface.

Track link having sensor-receiving cavities

A system may comprise a device. The device may be configured to receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; and predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components. The machine learning model is trained, using training data, to predict the wear rate of the one or more components. The training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices. The device may perform an action based on the amount of wear.

Undercarriage wear prediction using machine learning model

A system may include a device. The device may be configured to receive machine vibration data identifying a measure of vibration of a machine. The vibration, of the machine, may be caused by a combination of first vibration caused by a motion of components of an undercarriage of the machine and second vibration that is unrelated to the first vibration. The device may be configured to identify a segment, of the machine vibration data, corresponding to the first vibration; transform the segment, using a Fast Fourier Transform (FFT), into a signal in a frequency domain; and analyze the signal to identify a signature spectrum associated with the motion of components. The device may be configured to predict, based on the signature spectrum, an amount of wear of the components. The device may be configured to cause an action to be performed based on the amount of wear of the components.

Undercarriage wear prediction based on machine vibration data

A seal ring for a seal assembly includes a body and a seal flange. The body is generally cylindrical and extends along a longitudinal axis between a load end and a seal end. The seal flange is disposed at the seal end of the cylindrical body. The seal flange circumscribes the body and projects radially from the body to a distal perimeter of the seal flange. The seal flange includes a sealing face which is annular and disposed adjacent the distal perimeter. The seal ring is made from an alloy that includes between 6 percent and 9 percent by weight of iron, between 1.5 percent and 3 percent by weight of silicon, greater than 14 percent by weight of chromium, and at least 65 percent by weight of nickel.

Alloy for seal ring, seal ring, and method of making seal ring for seal assembly of machine

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Dunlap, IL, United States of America

Li Zhang