Design of Online Vision Detection System for Stator Winding Coil

• Published in: 东华大学学报（英文版） Vol. 40; no. 6; pp. 639 - 648
• Main Authors: LI Yan, LI Rui, XU Yang
• Format: Journal Article
• Language: English
• Published: 31.12.2023
• Subjects: V2-YOLOv5s model; online detection; stator winding coil; Canny algorithm; machine vision
• ISSN: 1672-5220
• DOI: 10.19884/j.1672-5220.202302008

TP391; The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed.A vision detection platform was designed to capture individual winding images,and an image processing algorithm was used for image pre-processing,template matching and positioning of the coil lead area to set up a coordinate system.After eliminating image noise by Blob analysis,the improved Canny algorithm was used to detect the location of the coil lead paint stripped region,and the time was reduced by about half compared to the Canny algorithm.The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset,and the detect file was converted to the Open Neural Network Exchange(ONNX)model for the detection of winding cross features with an average accuracy of 99.0%.The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces,and the detection data were recorded and statistically analyzed.The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%,and the average detection time of a single workpiece was about 300 ms,while YOLOv5s took less than 30 ms.