Methods and systems for verification of machine learning-based varnish analysis

Patent metadata

Field	Value
Publication number	US-12379716-B2
Application number	US-202217937664-A
Country	US
Kind code	B2
Filing date	Oct 3, 2022
Priority date	Oct 3, 2022
Publication date	Aug 5, 2025
Grant date	Aug 5, 2025

Abstract

Methods and systems are provided for verifying a deep learning tool for evaluating a varnish condition of a stator. In one example, a method for verifying the deep learning tool includes receiving images of replicas of a stator section at a processor of a computing system, the replicas of the stator section having different predetermined varnish fill percentages. The images are process and analyzed by the deep learning tool to output estimated varnish fill percentages. The estimated varnish fill percentages may be compared to the predetermined varnish fill percentages and a notification recommending at least one of further training of the deep learning tool and adjustments to an imaging setup for acquiring the images.

First claim

The invention claimed is:

1. A system for evaluating a deep learning tool for estimating varnish fill percentages of a stator, comprising:
a housing enclosing a UV light source and digital imaging equipment;
one or more replicas of a section of the stator having varnish configured to fluoresce when irradiated by the UV light source, the one or more replicas having amounts of varnish corresponding to predetermined varnish fill percentages; and
a processor configured with the deep learning tool and instructions stored on non-transitory memory that, when executed, cause the processor to:
receive images of the one or more replicas from the digital imaging equipment;
process and analyze the images using the deep learning tool by cropping the images and analyzing a fluorescence signature of the images, the deep learning tool trained to identify and quantify the varnish using deep learning algorithms;
output estimated varnish fill percentages from the deep learning tool;
compare the estimated varnish fill percentages to the predetermined varnish fill percentages; and
display a notification to a user at a display device in response to a difference between the estimated varnish fill percentages and the predetermined varnish fill percentages being greater than a threshold difference.

2. The system of claim 1 , wherein the section of the stator includes a transverse cross-section of the stator, the transverse cross-section obtained by slicing the stator along a plane perpendicular to a central axis of rotation of the stator.

3. The system of claim 1 , wherein the section of the stator includes an axial cross-section of the stator, the axial cross-section obtained by slicing the stator along a plane parallel with a central axis of rotation of the stator through at least one slot of the stator.

4. The system of claim 1 , wherein the difference is an averaged difference between each of the estimated varnish fill percentages and a corresponding predetermined varnish fill percentage of the predetermined varnish fill percentages.

5. The system of claim 1 , wherein the notification is displayed when at least one difference between one of the estimated varnish fill percentages and a corresponding predetermined varnish fill percentage of the predetermined varnish fill percentages is greater than the threshold difference.

6. The system of claim 1 , wherein the one or more replicas are fabricated reproductions of the section of the stator, and wherein the one or more replicas are fabricated with shallow pockets in a slot portion of each of the one or more replicas.

7. The system of claim 6 , wherein the varnish is disposed in the shallow pockets of each of the one or more replicas according to one of the predetermined varnish fill percentages, and wherein the varnish is disposed in equal or unequal amounts amongst the shallow pockets for a respective replica of the one or more replicas.

Assignees

• Ford Global Tech Llc

Inventors

• Schroeter Robert
• Siffre Ife

Classifications

• G05B19/4187

  by tool management · CPC title

• G06T7/0004

  Industrial image inspection · CPC title

• G06T2207/30164

  Workpiece; Machine component · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/20081

  Training; Learning · CPC title