Detecting Gear Surface Defects Using Background-Weakening Method and Convolutional Neural Network

Yu, Liya; Wang, Zheng; Duan, Zhongjing

doi:10.1155/2019/3140980

Cited by 25 publications

(14 citation statements)

References 32 publications

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“…R-CNN algorithm is also often used (more than 13%), for example, in Tabernik et al (2019), Shi et al (2020a), Liyun et al (2020), Zhao et al (2020a), Zhang & Shen (2021) or Zhao et al (2021). Moreover, there are articles focusing on the implementation of Contextual Hopfield Neural Network (Chang et al, 2011), Sparse Convolutional Neural Networks (Bella et al, 2019), FCN (Zhang et al, 2019), ResNet50 (Konovalenko et al, 2020), VGG16 (Ihar et al, 2019), YOLOv3 (Yu et al, 2019). The rest of the studies focused on NN can be seen in Table 7.…”

Section: • Neural Networkmentioning

confidence: 99%

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Bártová

Bína

Váchová

2022

Prod.

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Paper aims: This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations.Originality: Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing.Research method: In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004). Main findings:The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used. Implications for theory and practice:This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose.

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Section: • Neural Networkmentioning

confidence: 99%

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Bártová

Bína

Váchová

2022

Prod.

View full text Add to dashboard Cite

show abstract

“…( f ) defects in green, yellow, orange bounding box are scratch, cratering, hump, respectively in carbody [ 23 ]. ( g ) Lack defect of gear [ 24 ]. ( h ) light leakage defect on mobile screen [ 25 ].…”

Section: Figurementioning

confidence: 99%

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

et al. 2020

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The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

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“…By improving the Faster R-CNN network model, Sun et al realized the detection of scratch, oil pollution, block, and grinning four kinds of wheel hub defects quickly and accurately [22]. By using the Faster R-CNN network model, Urbonas et al realized the automated analysis of branch, scratch, stain, and core four kinds of wood panel surface defects, providing a new automatic solution for the lumber and wood processing industry [23]. By using the Faster R-CNN network model, Wang et al realized the detection of scratch defects on turbine blades of automobile turbine engine, which proved the 2 Journal of Sensors effectiveness of the application of DCNNs in the industrial automated surface inspection field [24].…”

Section: Application Of the Industrial Inspection Based On Dcnnsmentioning

confidence: 99%

Offset Detection of Grate Trolley’s Side Plate Based on YOLOv4

Wang

Fang

et al. 2021

Journal of Sensors

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Side plate offset is one of the grate system faults. If it is not dealt with in time, some accidents will occur and economic losses will be made. Aiming at the problems like time-consuming, labour-wasting, and low intelligent by the side plate offset detection method manually, an autoside plate offset detection method is proposed, based on You Only Look Once version 4 (YOLOv4). Two cameras were fixed to collect the image information of the grate trolley’s side plate. With reference to the grate trolley’s operation, the offset judgment rules were set. YOLOv4 object detection algorithm was used to detect the side plate and trolley’s chassis frame in video frame images. A baseline was set according to the position information of the trolley’s chassis frame output by detection, and then, the position intervals between side plates and the baseline could be determined by calculation. According to the judgment rules, the scheme in this paper could detect the offset fault of the trolley’s side plate timely, and an alarm would be made automatically when faults are detected. Our video images of the trolley’s side plate were collected and sorted in Baogang Group sintering plant for testing. In this experiment, no error judgment was made, and the average detection and judgment time was 0.024 s. In this paper, rather than manually, the real-time automatic detection was realized to detect the offset fault of the trolley’s side plate so as to provide a new solution for offset detection of the grate trolley’s side plate.

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Detecting Gear Surface Defects Using Background-Weakening Method and Convolutional Neural Network

Cited by 25 publications

References 32 publications

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

Offset Detection of Grate Trolley’s Side Plate Based on YOLOv4

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