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US 11,599,103 B2Granted
Method and system for data driven machine diagnostics
AssigneeDodge Industrial, Inc.
InventorsStefan Rakuff, Jinmiao Huang
Life of this patent
Estimating the term…
Abstract
A system for data driven diagnostics of a machine including a machine learning model instantiated in a computer, the machine learning model being configured to: receive operational data of the machine; and process the operational data to determine machine diagnostics information. The machine learning model is trained using simulated defect information received from a simulation environment.
Claims18
A system for data driven diagnostics of a gearbox, the system comprising:
- a gearbox comprising a housing, a first shaft and a second shaft each extending into and protruding outwardly from the housing, the first shaft being supported for rotation by a first bearing pair, the second shaft being supported for rotation by a second bearing pair, a third shaft disposed in the housing and being supported by a third bearing pair, a first gear disposed in the housing and mounted on the first shaft, a second gear disposed in the housing and mounted on the second shaft, a third gear and a fourth gear each mounted on the third shaft, the third gear meshing with the first gear and the fourth gear meshing with the second gear;
- a plurality of sensors each positioned in the housing;
- and
- a processor configured to run a machine learning model, the machine learning model being configured to receive operational data from at least one of the plurality of sensors, and process the operational data to determine diagnostic information of the gearbox,
- wherein the machine learning model is trained using simulated operational data from a defect simulation from a simulation environment of the gearbox,
- wherein the simulation environment comprises a finite element model of structural dynamics of the gear reducer and is configured to generate data pairs which comprise defect types and dynamic response signals, and
- wherein the learning model comprises a neural network that comprises an input layer that has a plurality of first nodes that correspond to the dynamic response signals, an intermediate layer configured to group similar sensor signals, and an output layer that has a plurality of second nodes that correspond to the defect types.
4 more elements
A method for data driven diagnostics of a gearbox, the method comprising:
- providing a gearbox comprising a housing, a first shaft and a second shaft each extending into and protruding outwardly from the housing, the first shaft being supported for rotation by a first bearing pair, the second shaft being supported for rotation by a second bearing pair, a third shaft disposed in the housing and being supported by a third bearing pair, a first gear disposed in the housing and mounted on the first shaft, a second gear disposed in the housing and mounted on the second shaft, a third gear and a fourth gear each mounted on the third shaft, the third gear meshing with the first gear and the fourth gear meshing with the second gear;
- providing a plurality of sensors each positioned in the housing;
- training a machine learning model with simulated operational data from a defect simulation from a simulation environment of the gearbox;
- receiving, by the machine learning model, operational data from at least one of the plurality of sensors;
- and
- processing, using the machine learning model, the operational data to determine diagnostic information of the gearbox,
- wherein the simulation environment comprises a finite element model of structural dynamics of the gear reducer and is configured to generate data pairs which comprise defect types and dynamic response signals, and
- wherein the learning model comprises a neural network that comprises an input layer that has a plurality of first nodes that correspond to the dynamic response signals, an intermediate layer configured to group similar sensor signals, and an output layer that has a plurality of second nodes that correspond to the defect types.
5 more elements
Dependencies are inferred from the claim text.