SYSTEMS AND METHODS OF ESTIMATING TORQUE, ROTATIONAL SPEED, AND OVERHUNG SHAFT FORCES USING A MACHINE LEARNING MODEL
A method of estimating an operating parameter of industrial mechanical power transmission equipment is provided. The method includes acquiring data of a first parameter of the gearbox using a sensor, inferring a second parameter of a gearbox based on the acquired data of the first parameter by using a machine learning model, wherein the second parameter is of a different type from the first parameter and includes at least one of a torque of the gearbox, a rotational speed of the gearbox, or an overhung shaft force of the gearbox, and outputting the estimated second parameter.
1 . A method of estimating an operating parameter of industrial mechanical power transmission equipment, comprising:
acquiring data of a first parameter of a gearbox using a sensor;
inferring a second parameter of the gearbox based on the acquired data of the first parameter by using a machine learning model, wherein the second parameter is of a different type from the first parameter and includes at least one of a torque of the gearbox, a rotational speed of the gearbox, or an overhung shaft force of the gearbox; and
outputting the estimated second parameter.
2 . The method of claim 1 , wherein the data include a time series of data points, and estimating a second parameter further comprises:
generating a frequency spectrum of the data by Fourier transforming the time series; and
inferring the second parameter based on the frequency spectrum of the data.
3 . The method of claim 1 , wherein:
acquiring data further comprises:
acquiring first data of the first parameter of the gearbox using a first sensor; and
acquiring second data of the first parameter of an ambient environment of the gearbox using a second sensor; and
estimating a second parameter further comprises:
decoupling the first data from the second data by removing an ambient condition of the ambient environment in the first data; and
estimating the second parameter based on the decoupled first data.
4 . The method of claim 1 , wherein estimating a second parameter further comprises:
identifying an algorithm of the machine learning model.
5 . The method of claim 1 , wherein acquiring data further comprises acquiring data using a plurality of sensors.
6 . The method of claim 1 , wherein the sensor is a vibration sensor.
7 . The method of claim 1 , wherein the sensor is a sound pressure sensor.
8 . The method of claim 1 , wherein the second parameter is the torque of the gearbox.
9 . The method of claim 1 , wherein the second parameter is the rotational speed of the gearbox.
10 . The method of claim 1 , wherein the second parameter is the overhung shaft forces of the gearbox.
11 . The method of claim 1 , wherein the sensor is mounted on the gearbox.
12 . The method of claim 1 , further comprising repeating acquiring data and estimating a second parameter for a plurality of times, wherein the method further comprises:
generating an output second parameter using estimated second parameters based on a predetermined rule; and
outputting the output second parameter.
13 . A parameter estimation system for industrial mechanical power transmission equipment, comprising a parameter estimation computing device, the parameter estimation computing device comprising at least one processor in communication with at least one memory device, and the at least one processor programmed to:
receive data of a first parameter of the power transmission equipment acquired by using a sensor;
estimate a second parameter of the power transmission equipment based on the received data of the first parameter by using a machine learning model, wherein the second parameter is of a different type from the first parameter and includes at least one of a torque of the power transmission equipment, a rotational speed of the power transmission equipment, or an overhung shaft force of the power transmission equipment; and
output the estimated second parameter.
14 . The system of claim 13 , wherein the data is a time series of data points, and the at least one processor is further configured to:
generate a frequency spectrum of the data by Fourier transforming the time series; and
estimate the second parameter based on the frequency spectrum of the data.
15 . The system of claim 13 , wherein the power transmission equipment is a gearbox.
16 . The system of claim 13 , wherein the sensor is a vibration sensor.
17 . The system of claim 13 , wherein the sensor is a sound pressure sensor.
18 . The system of claim 13 , wherein the second parameter is the torque of the power transmission equipment.
19 . The system of claim 13 , wherein the second parameter is the rotational speed of the power transmission equipment.
20 . The system of claim 13 , wherein the second parameter is the overhung shaft force of the power transmission equipment.