Detection of defects in the manufacturing of electric motor stators using vision systems: Electrical connectors

Pacheco, Antonio Luiz Schalata; Flesch, Rodolfo C.C.; Demay, Miguel Burg

doi:10.1109/induscon.2016.7874551

Cited by 10 publications

(18 citation statements)

References 8 publications

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“…It has been reported in literature that artificial intelligence [1] can be utilized to collect and analyze the fault mechanism of motor insulation, and image recognition can assist in the inspection of the motor's rotor appearance [2]. Artificial intelligence can also collect vibration signals through machine learning-based techniques [3] to evaluate sound data and implement automatic fault detection functions.…”

Section: Research Backgroundmentioning

confidence: 99%

“…After confirmation, ensure that the machine is inserted according to the direction. (2) Press the button for the machine to start winding. (3)…”

Section: Windingmentioning

confidence: 99%

“…(1) Place the coil into the machine according to the direction. (2) Press the button to allow the machine to start the spot welding. 3Check whether the appearance of parts is complete and if there are five grooves.…”

Section: Commutator Spot Weldingmentioning

confidence: 99%

“…(1) Place the parts in the machine. (2) Clean the parts with a brush. 3Use a magnifying glass to check the appearance of parts and for foreign objects.…”

Section: Pneumatic Rectificationmentioning

confidence: 99%

“…(1) Place the bearing and coil in the machine in the same direction. (2) Press the switch to initiate punching. (3) Check whether the parts are intact and correctly aligned.…”

mentioning

confidence: 99%

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Implementation Criteria for Intelligent Systems in Motor Production Line Process Management

et al. 2020

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In this study, the factors that affect the implementation of intelligent systems in motor production lines are analyzed. A motor production line located in Vietnam is used as the research object. The research methods include secondary data collection, field study, and interviews. This study demonstrates the following: firstly, the implementation of intelligent systems in motor production lines is heading toward Industry 4.0. Secondly, it is proposed that three functional systems—robot arm, image recognition, and big data analysis—can be introduced in the motor production line. This study analyzes the process involved in coil and motor production lines and attempts to combine intelligent system functions. It is expected that in the future, manpower will be reduced, production line productivity will increase, and intelligent production lines will be proposed. The factors that affect the introduction of intelligent systems in motor production lines are improved, and the importance of intelligent systems, which has been rarely considered in previous studies, is highlighted. In the implementation criteria of the intelligent system in the process management of the motor production line, this study provides some suggestions (to coil and motor assembly line) for the production process management. These suggestions can be provided as a reference for production lines that acquaint with intelligent systems.

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Section: Research Backgroundmentioning

confidence: 99%

“…After confirmation, ensure that the machine is inserted according to the direction. (2) Press the button for the machine to start winding. (3)…”

Section: Windingmentioning

confidence: 99%

Section: Commutator Spot Weldingmentioning

confidence: 99%

“…(1) Place the parts in the machine. (2) Clean the parts with a brush. 3Use a magnifying glass to check the appearance of parts and for foreign objects.…”

Section: Pneumatic Rectificationmentioning

confidence: 99%

“…(1) Place the bearing and coil in the machine in the same direction. (2) Press the switch to initiate punching. (3) Check whether the parts are intact and correctly aligned.…”

mentioning

confidence: 99%

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Implementation Criteria for Intelligent Systems in Motor Production Line Process Management

et al. 2020

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Augmented classification for electrical coil winding defects

Farnsworth

Tiwari

Zhang

et al. 2022

Int J Adv Manuf Technol

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A green revolution has accelerated over the recent decades with a look to replace existing transportation power solutions through the adoption of greener electrical alternatives. In parallel the digitisation of manufacturing has enabled progress in the tracking and traceability of processes and improvements in fault detection and classification. This paper explores electrical machine manufacture and the challenges faced in identifying failures modes during this life cycle through the demonstration of state-of-the-art machine vision methods for the classification of electrical coil winding defects. We demonstrate how recent generative adversarial networks can be used to augment training of these models to further improve their accuracy for this challenging task. Our approach utilises pre-processing and dimensionality reduction to boost performance of the model from a standard convolutional neural network (CNN) leading to a significant increase in accuracy.

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A robust approach for industrial small-object detection using an improved faster regional convolutional neural network

Saeed

Ahmed

Gul

et al. 2021

Sci Rep

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With the increasing pace in the industrial sector, the need for a smart environment is also increasing and the production of industrial products in terms of quality always matters. There is a strong burden on the industrial environment to continue to reduce impulsive downtime, concert deprivation, and safety risks, which needs an efficient solution to detect and improve potential obligations as soon as possible. The systems working in industrial environments for generating industrial products are very fast and generate products rapidly, sometimes leading to faulty products. Therefore, this problem needs to be solved efficiently. Considering this problem in terms of faulty small-object detection, this study proposed an improved faster regional convolutional neural network-based model to detect the faults in the product images. We introduced a novel data-augmentation method along with a bi-cubic interpolation-based feature amplification method. A center loss is also introduced in the loss function to decrease the inter-class similarity issue. The experimental results show that the proposed improved model achieved better classification accuracy for detecting our small faulty objects. The proposed model performs better than the state-of-the-art methods.

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Detection of defects in the manufacturing of electric motor stators using vision systems: Electrical connectors

Cited by 10 publications

References 8 publications

Implementation Criteria for Intelligent Systems in Motor Production Line Process Management

Implementation Criteria for Intelligent Systems in Motor Production Line Process Management

Augmented classification for electrical coil winding defects

A robust approach for industrial small-object detection using an improved faster regional convolutional neural network

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