Defect inspection in stator windings of induction motors based on convolutional neural networks

Seibert, Artur Antonio; Fröhlich, Herberth Birck; Costa, Leonardo Rocha Carnauba da; Lopes, Liasse Birck; Iervolino, Lucas Arrigoni; Demay, Miguel Burg; Flesch, Rodolfo C.C.

doi:10.1109/induscon.2018.8627172

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…To achieve quality control, techniques for automatic inspections during manufacture by utilising vision systems has been proposed by Oliveira et al 25 in which a vision system based on edge-detection tools was developed to identify defects such as coil segments of the winding that are not properly fastened to the other coils. The developed edge-detection tools had a limitation for large production volumes, and to deal with this the authors proposed a CNN based artificial intelligence method for detecting defects in stator windings of induction motors.…”

Section: Winding and Wiringmentioning

confidence: 99%

“…The developed edge-detection tools had a limitation for large production volumes, and to deal with this the authors proposed a CNN based artificial intelligence method for detecting defects in stator windings of induction motors. 25 Leo et al 26 proposed a vision system for online quality monitoring by performing dimensional measurements of critical lengths of copper wire during manufacturing. Another vision-based method to monitor the wire drawing process was proposed by Larsson et al, 27 that could potentially be utilised in inspection of insulation of wire before starting the winding process.…”

Section: Winding and Wiringmentioning

confidence: 99%

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In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions

Tiwari

Farnsworth

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Manual operations feature prominently in the manufacture of many electrical machines. Even though high-volume electrical machine manufacture is dominated by automation, several manufacturing operations continue to involve manual intervention because of the complexity of such operations makes them heavily reliant on high dexterity manual skills and experience. However, quality can be variable due to human involvement. Currently, in order to maintain a high precision of control and required tolerances of the final machine, inspection is performed at various steps during manufacturing and assembly. Detecting a defect at these end-of-line tests can result in significant wasted time and costs due to rework or scrappage. The solution to this problem lies in in-process monitoring particularly for error prone manual operations. This paper presents a literature review of the state-of-the-art available techniques and limitations in process monitoring within the context of electrical machine manufacturing. To quantify the degree of manual activities in process monitoring within electrical machine manufacture, a structured survey of UK based companies was conducted, identifying specific error prone manual processes to target, and gaps in inspection. The survey identified that a significant proportion of activities in electrical machine manufacture are manual, or semi-automated with manual interventions. However, literature review revealed only a limited research in in-process monitoring of manual operations in this area. Finally, two case studies are presented where case study 1 presents a framework for digitisation of a variety of manual manufacturing tasks, and case study 2 demonstrates real-time capture, modelling and analysis of deformable linear objects in electrical machine manufacturing.

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Section: Winding and Wiringmentioning

confidence: 99%

Section: Winding and Wiringmentioning

confidence: 99%