Real‐time fabric defect detection based on multi‐scale convolutional neural network

Zhao, Shuxuan; Li, Yin; Zhang, Jie; Wang, Junliang; Zhong, Ray Y.

doi:10.1049/iet-cim.2020.0062

Cited by 27 publications

(9 citation statements)

References 32 publications

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“…Different natural modal components contain different frequency components, and as the order of IMF increases, the frequency components contained in them gradually decrease, and the lowest frequency component is rn(t). The first few IMF components obtained by empirical mode decomposition usually contain most of the information of the original signal [18][19][20] . Combined with the long period of the trend term, that is, the characteristics of low frequency, the first few IMF components are summed to obtain an approximate signal of the original signal.…”

Section: Principles Of Generative Adversarial Network (Gan)mentioning

confidence: 99%

“…The research team from Henan University of Science and Technology used fewer sample data for wind turbine gearbox fault diagnosis and utilised the least squares support vector machine method to achieve wind turbine gearbox faults, and the results show that the proposed method can effectively improve the speed of diagnosis. There are many fault diagnosis methods for wind turbines [15][16][17][18][19][20] . In the wind turbine gearbox fault diagnosis method, the accurate selection of fault features is the key to establish an efficient fault diagnosis method.…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

Meng,

An,

Zhang

et al. 2024

EAI Endorsed Trans Energy Web

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INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper. OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy. METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis. RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.

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Section: Principles Of Generative Adversarial Network (Gan)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

Meng,

An,

Zhang

et al. 2024

EAI Endorsed Trans Energy Web

View full text Add to dashboard Cite

show abstract

“…Detection approaches utilize bounding boxes to indicate the defect locations in fabric images. [31][32][33] To improve accuracy and time efficiency, Zhao et al 34 proposed a method based on a multi-scale convolutional neural network, while Peng et al 35 introduced prior anchor boxes and a feature pyramid structure in their model. Detection approaches are constrained by artificially set candidate frames, which limits their ability to describe the scale of defects accurately.…”

Section: Related Workmentioning

confidence: 99%

Dual-path segmentation network for automatic fabric defect detection

Yu,

Xu,

Wang

et al. 2023

Textile Research Journal

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Fabric defect detection plays a crucial role in the production process of the textile industry. Vision-based inspection methods have emerged as an inevitable trend due to their lower labor costs and high detection efficiency. As the accuracy requirements for fabric defect detection, methods must not only identify and locate defects accurately but also describe the morphological features of the defects. This poses a challenge for the algorithm’s design, as it must consider both the semantic and texture information of the fabric. In this paper, we propose an end-to-end dual-path segmentation network called DPNet for fabric defect detection, which can extract and fuse both semantic and texture information to achieve high accuracy. The proposed framework consists of two paths: the semantic path, which has narrow but deep layers to obtain high-dimensional features, and the texture path, which has wide and dense layers to extract low-level details. To enhance the interaction between semantic and texture features, a crossed attention fusion module has been developed. Evaluations show that the proposed method outperforms other methods on different datasets in terms of mIoU, with results of 75.84% for Ngan and 70.69% for AITEX. In addition, we developed an inspection platform and tested the proposed method online. We found that it can achieve online detection at a speed of 40 m/min, making it well-suited for practical production environments.

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“…This model can effectively extract deep features from fault signals with high similarity. Han et al proposed an improved DBN algorithm for GFD based on wavelet packet energy entropy (WPEE) and MPE [86]. The algorithm can extract deep features of signals and classify them.…”

Section: Deep Belief Networkmentioning

confidence: 99%

“…Han et al. proposed an improved DBN algorithm for GFD based on wavelet packet energy entropy (WPEE) and MPE [86]. The algorithm can extract deep features of signals and classify them.…”

Section: Fault Recognitionmentioning

confidence: 99%

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Zhang

Zhou

Wang

et al. 2022

IET Collab Intel Manufact

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Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

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Real‐time fabric defect detection based on multi‐scale convolutional neural network

Cited by 27 publications

References 32 publications

Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

Dual-path segmentation network for automatic fabric defect detection

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

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