System, apparatus and method for estimating remaining useful life of a bearing

Patent metadata

Field	Value
Publication number	US-11921005-B2
Application number	US-202117459326-A
Country	US
Kind code	B2
Filing date	Aug 27, 2021
Priority date	Aug 28, 2020
Publication date	Mar 5, 2024
Grant date	Mar 5, 2024

Abstract

A system, apparatus, and method for estimating remaining useful life of a bearing are provided. The method includes receiving a request for analyzing a defect in the bearing from a source. The request includes operational data associated with the bearing. An impact of the defect on the bearing is monitored over a period of time. A time period during which the impact of the defect on the bearing is higher than a threshold range is determined using a machine learning model. A severity of the impact associated with the defect is computed during the time period. A remaining useful life of the bearing is determined based on the severity and the operational data during the time period.

First claim

The invention claimed is:

1. A computer-implemented method for estimating remaining useful life of a bearing, the computer-implemented method comprising:
receiving, by a processing unit, a request for analyzing a defect in the bearing from a source, wherein the request comprises operational data associated with the bearing;
monitoring an impact of the defect on the bearing over a period of time;
determining a time period during which the impact of the defect on the bearing is higher than a threshold range using a machine learning model, the time period being indicative of a time duration in which the bearing enters and leaves the defect;
computing a severity of the impact associated with the defect during the time period by computing a defect size corresponding to the defect based on a duration of the time period;
determining, using a virtual model of the bearing, a remaining useful life of the bearing based on the severity and the operational data during the time period, wherein the virtual model is generated based on simulation data, experimental data, mathematical data, or any combination thereof associated with a plurality of other bearings; and
generating a notification indicating the remaining useful life of the bearing on an output device.

2. The method of claim 1 , wherein the operational data comprises an output of at least one sensing unit associated with the bearing in real-time.

3. The method of claim 1 , wherein monitoring the impact of the defect on the bearing over a period of time comprises:
monitoring anomalies in the output of the at least one sensing unit.

4. The method of claim 1 , wherein determining the time period during which the impact of the defect on the bearing is higher than a threshold range using the machine learning model comprises:
analyzing the operational data associated with the bearing using the machine learning model for determining the time period.

5. The method of claim 1 , wherein determining the remaining useful life of the bearing based on the severity and the operational data during the time period comprises:
computing a dynamic parameter associated with the bearing based on the defect size and the operational data using the virtual model of the bearing;
configuring a remaining useful life model of the bearing based on the dynamic parameter; and
computing the remaining useful life of the bearing based on the configured remaining useful life model and the operational data.

6. An apparatus for estimating remaining useful life of a bearing, the apparatus comprising:
one or more processing units; and
a memory unit communicatively coupled to the one or more processing units, wherein the memory unit comprises a bearing management module stored in the form of machine-readable instructions executable by the one or more processing units to estimate remaining useful life of a bearing, the machine-readable instructions comprising:
receiving, by the one or more processing units, a request for analyzing a defect in the bearing from a source, wherein the request comprises operational data associated with the bearing;
monitoring an impact of the defect on the bearing over a period of time;
determining a time period during which the impact of the defect on the bearing is higher than a threshold range using a machine learning model, the time period being indicative of a time duration in which the bearing enters and leaves the defect;
computing a severity of the impact associated with the defect during the time period by computing a defect size corresponding to the defect based on a duration of the time period;
determining, using a virtual model of the bearing, a remaining useful life of the bearing based on the severity and the operational data during the time period, wherein the virtual model is generated based on simulation data, experimental data, mathematical data, or any combination thereof associated with a plurality of other bearings; and
generating a notification indicating the remaining useful life of the bearing on an output device.

7. A system for estimating remaining useful life of a bearing, the system comprising:
one or more sources configured to provide operational data associated with the bearing; and
an apparatus communicatively coupled to the one or more sources, wherein the apparatus is configured to estimate remaining useful life of the bearing based on the operational data, the estimation of the remaining useful life of the bearing comprising:
receipt, by a processing unit, a request for analysis of a defect in the bearing from a source, wherein the request comprises operational data associated with the bearing;
monitor of an impact of the defect on the bearing over a period of time;
determination of a time period during which the impact of the defect on the bearing is higher than a threshold range using a machine learning model, the time period being indicative of a time duration in which the bearing enters and leaves the defect;
computation of a severity of the impact associated with the defect during the time period by computation of a defect size corresponding to the defect based on a duration of the time period;
determination, using a virtual model of the bearing, of a remaining useful life of the bearing based on the severity and the operational data during the time period, wherein the virtual model is generated based on simulation data, experimental data, mathematical data, or any combination thereof associated with a plurality of other bearings; and
generation of a notification indicating the remaining useful life of the bearing on an output device.

8. In a non-transitory computer-readable storage medium that stores instructions executable by a data-processing system to estimate remaining useful life of a bearing, the instructions comprising:
receiving, by a processing unit, a request for analyzing a defect in the bearing from a source, wherein the request comprises operational data associated with the bearing; monitoring an impact of the defect on the bearing over a period of time;
determining a time period during which the impact of the defect on the bearing is higher than a threshold range using a machine learning model, the time period being indicative of a time duration in which the bearing enters and leaves the defect;
computing a severity of the impact associated with the defect during the time period by computing a defect size corresponding to the defect based on a duration of the time period;
determining, using a virtual model of the bearing, a remaining useful life of the bearing based on the severity and the operational data during the time period, wherein the virtual model is generated based on simulation data, experimental data, mathematical data, or any combination thereof associated with a plurality of other bearings; and
generating a notification indicating the remaining useful life of the bearing on an output device.

9. The non-transitory computer-readable storage medium of claim 8 , wherein the operational data comprises an output of at least one sensing unit associated with the bearing in real-time.

10. The non-transitory computer-readable storage medium of claim 8 , wherein monitoring the impact of the defect on the bearing over a period of time comprises:
monitoring anomalies in the output of the at least one sensing unit.

11. The non-transitory computer-readable storage medium of claim 8 , wherein determining the time period during which the impact of the defect on the bearing is higher than a threshold range using the machine learning model comprises:
analyzing the operational data associated with the bearing using the machine learning model for determining the time period.

Assignees

• Nair Sudev
• Ramasamy Iniyan
• Siemens Ag

Inventors

• Nair Sudev
• Ramasamy Iniyan

Classifications

• G01M13/045 Primary

  Acoustic or vibration analysis · CPC title

• G01N19/08

  Detecting presence of flaws or irregularities · CPC title

• G06F30/17 Primary

  Mechanical parametric or variational design · CPC title

• G01M13/04 Primary

  Bearings · CPC title