Ford Global Technolgoies, LLC

Robert Schroeter

Mahmoud Yousef Ghannam

Justin Miller, Jr

Jake S Schwartz

Jackson Lenz

Boratha Tan

Chris Wolf

Seth Avery

Ife Siffre

Aaron Lewis Mills

Arun Dutta

Anna Marie-Sophia Hoff

Michael Oelscher

Methods and systems are provided for an insulation system of a stator. In one example, a method may include receiving images of the stator at an automated tool implemented at a processor of a computing system, the images depicting slots in in an inner surface of the stator, and processing the images using image processing and deep learning algorithms to provide processed images. The processed images may be input to an artificial intelligence (AI) model of the automated tool, the AI model trained to identify varnish in the processed images. obtained two-dimensional presentation of varnish distribution may be output from the AI model.

Methods and systems for non-destructive evaluation of stator insulation condition

A detection system for a target vehicle includes a camera capturing images of a region external to the target vehicle, a RADAR module that scans the region to detect a depth of a splash event in the region, an actuator configured to operate in response to a response signal and control circuitry configured to determine a distance between the target vehicle and a front of the splash event based on the images, compare the front of the splash event to the depth to determine an intensity of the splash event, and communicate the response signal based on the intensity of the splash event.

Splash detection using radar

A method includes obtaining pre-weld image data of the electric motor stator from one or more image sensors; performing a welding process in response to obtaining the pre-weld image data, obtaining post-weld image data of the electric motor stator from the one or more image sensors in response to performing the welding process, obtaining epoxy image data of the electric motor stator from the one or more image sensors in response to obtaining the post-weld image data, performing a difference-based image processing routine based on the post-weld image data and the epoxy image data to generate a digital twin of the electric motor stator, and determining one or more epoxy characteristics of the electric motor stator based on the digital twin.

Systems and methods for non-destructively testing stator weld quality and epoxy thickness

A vehicle includes a window separating a compartment of the vehicle from a region external to the vehicle. The window includes an outer surface. An imaging device is in the compartment and configured to capture an image of the region external to the vehicle through the window. Control circuitry is in communication with the imaging device and configured to receive the image from the imaging device, detect an obstruction in the image data corresponding to a substance on the window, and generate an output in response to the detection of the obstruction, wherein the imaging device is disposed within 50 millimeters (mm) from the window.

Visibility obstruction detection for a vehicle

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.

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

Methods and systems are provided for an insulation system of a stator. In one example, a method may include receiving images of the stator at a processor of a computing system and feeding the images to a deep learning tool to generate processed images by segmenting and cropping the images according to slots identified in the images. Further, the varnish in the processed images may be quantified based on fluorescence of the varnish, converted into estimated varnish fill percentages, based on an output from analysis of the processed images, and the estimated varnish fill percentages may be displayed in a report.

Methods and systems for predicting stator insulation condition from stator sections

A vehicle includes a compartment having a first space having a first environmental condition and a second space having a second environmental condition. At least one camera captures a first image of a first part of a region external to the vehicle and a second image of a second part of the region. The first part of the region aligns with the first space, and the second part of the region aligns with the second space. An environmental control system of the vehicle has at least one of lighting control and climate control. Control circuitry is configured to determine brightness conditions of the region based on the first image and the second image and control the environmental control system to adjust the environmental conditions based on the brightness conditions.

Localized brightness detection for a vehicle

Methods and systems are provided for an insulation system of a stator. In one example, a method may include receiving images of the stator at a processor of a computing system and processing the images by converting the images to one or more of cluster-only images and binary masks. The images may be fed to an artificial intelligence (AI) model to obtain one or more of varnish estimates and void estimates to generate a training dataset which may be used to train a deep learning tool to estimate varnish fill percentages from the images and display the varnish fill percentages at a display device.

Methods and systems for varnish analysis of stator images

A vehicle includes at least one camera that captures at least one image of a region external to the vehicle. The vehicle includes an environmental control system of the vehicle having at least one of lighting control and climate control for the vehicle. The vehicle includes control circuitry configured to process a first set of images of the plurality of images at a first sampling rate, determine a brightness condition of the region external to the vehicle based on processing the first set of images, process a second set of images of the plurality of images at a second sampling rate different than the first sampling rate, detect a shift in the brightness condition based on the second set of images, and control the environmental control system in response to the shift in the brightness condition.

Vehicle environment control using brightness detection

A brightness management system for a vehicle includes a network providing wireless communication for a vehicle environment, a mobile device in the vehicle environment and including a first light detector for detecting a first light level at a first location of the mobile device, at least one vehicle display coupled with the vehicle and having at least one brightness level, and control circuitry communicatively coupled with the mobile device and the at least one vehicle display via the network. The control circuitry is configured to detect the first location of the mobile device in the vehicle, receive an indication of the first light level from the mobile device, and communicate a signal to adjust the at least one brightness level of the at least one vehicle display based on the first location and the first light level.

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Livonia, MI, United States of America

Robert Schroeter