Electric Motor Winding Inspection

Prevent inefficient motors by detecting potential winding errors with deep learning solutions

Cognex deep learning ensures winding coils are properly assembled in an electric vehicle motor

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In an electric motor, insulated copper wire is wound around a core to create or receive electromagnetic energy, transferring that energy by induction to another coil. Such coils are also found in converters. These coils are rapidly wound by a machine.

The windings in electric vehicle (EV) motors are extremely dense. Any inaccuracies in how they were wound can have a negative effect on the motor’s efficiency. Given the vast number of windings crammed into a narrow space, even small winding errors can be significant, but hard to identify. The winding error may be subtle and can occur anywhere among the many visible wires.

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There is no efficient way to code a rule-based machine vision system to cover all the winding error possibilities anywhere on the coil. Human inspection is also not suited for identifying such subtle errors in a complex image.

Cognex Deep Learning using a color camera accurately verifies that the winding process has been accomplished without error. The defect detection tool learns from a set of training images consisting of error-free windings and labeled images featuring a wide range of overlaps, mispositionings, crossings, and other potential errors in various locations.

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