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Rolling bearing fault diagnosis based on gramian angular difference field and improved channel attention model

PEERJ COMPUTER SCIENCE 2024年 10卷 e1807页

作者： Wei, Lunpan Peng, Xiuyan Cao, Yunpeng Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Heilongjiang Peoples R China Harbin Engn Univ Coll Power & Energy Engn Harbin Heilongjiang Peoples R China

Fault diagnosis of rolling bearings is a critical task, and in previous research, convolutional neural networks (CNN) have been used to process vibration signals and perform fault diagnosis. However, traditional CNN models have certain limitations in terms of accuracy. To improve accuracy, we propose a method that combines the gramian angular difference field (GADF) with residual networks (ResNet) and embeds frequency channel attention module (Fca) in the ResNet to diagnose rolling bearing fault. Firstly, we used GADF to convert the signals into RGB three-channel fault images during data preprocessing. Secondly, to further enhance the performance of the model, on the foundation of the ResNet we embedded the frequency channel attention module with discrete cosine transform (DCT) to form Fca, to effectively explores the channel information of fault images and identifies the corresponding fault characteristics. Finally, the experiment validated that the accuracy of the new model reaches 99.3% and the accuracy reaches 98.6% even under an unbalanced data set, which significantly improves the accuracy of fault diagnosis and the generalization of the model.

关键词： Fault diagnosis Rolling bearings gramian angular difference field Channel attention module Residual networks

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Fault Identification Method of Transformer Winding based on gramian angular difference field and Convolutional Neural Network

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2024年第8期17卷 837-847页

作者： Yang, Shihao Li, Zhenhua Yang, Xinqiang Wu, Hairong China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

Background As the frequency of transformer winding faults becomes higher and higher, the frequency response analysis used to detect the winding status has attracted more and more attention. At present, there is still a lack of reliable and intelligent technologies for detecting the state of transformer windings in this *** This paper focuses on studying a high-precision method for transformer fault diagnosis, which can be easily and effectively applied to daily *** By changing the detection method, the traditional detection method can not distinguish the problem that the detection data are highly overlapping when identifying the same fault of the head and tail symmetric points, and the problem that the phase is too similar is changed. In order to solve the problem that the fault samples of transformer frequency response curve are scarce and the one-dimensional data cannot be read by partial deep learning method, the one-dimensional data of frequency response curve is first converted into characteristic index and then into a three-dimensional image by moving window calculation method and gramian angular difference field transformation. The fault classification is realized by a convolutional neural *** The accuracy of the final model for slice classification reached 100%.Conclusion Illustrative examples show that the method is distinguishable from different fault types. The traditional method only uses the amplitude of the frequency response curve, but this method displays the two features of the amplitude-phase together in the image. Compared with the traditional method, more features and samples are added to further improve the accuracy of the method. The accuracy of diagnosis results reached 100%, which showed the feasibility of the method.

关键词： Transformer winding fault diagnosis gramian angular difference field convolutional neural network moving windows frequency response

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Gearbox fault diagnosis based on frequency-domain gramian angular difference field and deep convolutional neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2023年第21期237卷 5187-5202页

作者： Zhang, Jianqun Zhang, Qing Feng, Wenzong Qin, Xianrong Sun, Yuantao Tongji Univ Sch Mech Engn Shanghai Peoples R China Tongji Univ Sch Mech Engn 4800 Caoan Rd Shanghai 201804 Peoples R China

Gearbox fault diagnosis is of great significance to avoid accidents and reduce operation and maintenance costs for machinery such as the quayside container crane. In recent years, mechanical fault diagnosis methods based on deep learning have developed rapidly. How to perform diagnosis simply based on deep learning in the case of limited labeled samples is still a challenging problem. To address this issue, this paper proposes a new fault diagnosis method combining the frequency-domain gramian angular difference field (FDGADF) and deep convolutional neural network (DCNN), namely, FDGADF-DCNN. To explore the availability of the FDGADF-DCNN method, two experiment datasets are studied. When the proportion of labeled samples is 70%, the recognition accuracy of both datasets exceeds 98%. When only 20% of the labeled samples are available (the average number of training samples in each category is only dozens), FDGADF-DCNN can achieve a recognition accuracy of no less than 95% in two datasets, in which a benchmark dataset is 100% and the dataset collected from a scaled-down test bench is 95%. Compared with other 11 methods, the FDGADF-DCNN method achieves the highest recognition accuracy. These results indicate that the FDGADF-DCNN method is expected to be used for gearbox fault diagnosis under limited labeled samples.

关键词： Fault diagnosis gramian angular difference field convolutional neural network limited labeled samples gearbox

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Combined gramian angular difference field image coding and improved mobile vision transformer for determination of apple soluble solids content by Vis-NIR spectroscopy

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2024年 131卷

作者： Li, You Sun, Hongwei Zheng, Yurui Wei, Qiquan Chen, Zhaoqing Zhang, Jianyi Qi, Hengnian Zhang, Chu Chen, Fengnong Hangzhou Dianzi Univ Sch Automat Sch Artificial Intelligence Hangzhou 310018 Peoples R China Huzhou Vocat & Tech Coll Sch Informat Engn & Internet Things Huzhou 313000 Peoples R China Zhejiang Dekfeller Intelligent Machinery Mfg Co Lt Yong Kang 321300 Peoples R China Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China

The soluble solids content (SSC) is an important indicator for evaluating the internal quality of apples. This study proposed a novel method for detecting apple SSC. This method involved collecting the transmission spectra of apples, transforming one-dimensional spectra into two-dimensional images using the gramian angular difference field (GADF) technique, and analyzing the images using an improved mobile vision transformer (MobileViT) model. Furthermore, the Grad-CAM method was used to visualize the model's prediction effect on SSC and find the wavelength range that had a greater impact on the model results. The results indicated that the GADFimproved MobileViT model exhibited excellent performance on the test dataset, with a determination coefficient (R2) of 0.938, a root mean square error (RMSE) of 0.532, and a mean absolute error (MAE) of 0.423. As the number of wavelengths used for modeling decreased, the improvements made in this study to MobileViT continued to enhance the model's performance effectively. And when using fewer training samples, the GADFimproved MobileViT model also had better performance than the original model. In conclusion, using visible and near-infrared spectroscopy and GADF image encoding techniques for the prediction of apple SSC is feasible.

关键词： Vis-NIR Soluble solids content gramian angular difference field MobileViT Coordinate attention

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Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS

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SENSORS 2024年第16期24卷 5438页

作者： Ren, Jiwen Xiong, Yuming Chen, Xinyu Hao, Yong East China Jiaotong Univ Sch Mechatron & Vehicle Engn Nanchang 330013 Peoples R China Changzhou Inst Technol Optoelect Dept Changzhou 213000 Peoples R China

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing the interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth of applications of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, have been progressively integrated into NIRS. In this paper, two discriminant analysis problems, including wheat kernels and Yali pears as examples, and several representative calibration models were used to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which was based on the gramian angular difference field method and coordinate attention convolutional neural networks (G-CACNNs). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results using the original spectrum. The accuracy of G-CACNNs in two discrimination tasks was 98.48% and 99.39%. Finally, this research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

关键词： gramian angular difference field convolutional neural networks near-infrared spectroscopy coordinate attention robust model

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EEG Emotion Recognition Based on GADF and AMB-CNN Model

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND fieldS 2024年第6期37卷

作者： Zhao, Qian Zhao, Dandan Yin, Wuliang Qufu Normal Univ Coll Engn Rizhao Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester England

Deep learning has achieved better results in natural language processing, computer vision, and other fields. Nowadays, more deep learning algorithms have also been applied in brain-based emotion recognition. In the studies on brain-based emotion recognition, deep learning models typically use one-dimensional time series as the input and cannot fully leverage the advantages of the models in image classification or recognition. To address this issue, based on the publicly available SEED and DEAP datasets, the gramian angular difference field (GADF) method was proposed to construct two-dimensional image representation datasets: SEED-GADF and DEAP-GADF datasets, in the paper. Additionally, a convolutional attention mechanism model (AMB-CNN) was introduced and its classification performance was validated on SEED-GADF and DEAP-GADF datasets. AMB-CNN achieved an average accuracy of 90.8%, a recall rate of 90%, and AUC of 96.86% on SEED-GADF. On DEAP-GADF, the average accuracy, recall rate, and AUC respectively reached 96.06%, 96.06%, and 98.58% in the valence dimension and 96.11%, 96.11%, and 98.73% in the arousal dimension. Finally, the comparison results with various algorithms and ablation experiments proved the superiority of the proposed model.

关键词： attention mechanism block CNN EEG emotion recognition gramian angular difference field

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Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

JOURNAL OF MEASUREMENTS IN ENGINEERING

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JOURNAL OF MEASUREMENTS IN ENGINEERING 2024年第2期12卷 333-352页

作者： Feng, Min Xu, Juncai Anhui Prov Int Joint Res Ctr Data Diag & Smart Mai Chuzhou 239099 Peoples R China Nanjing Med Univ Nanjing Rehabil Med Ctr Nanjing 210029 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 10098 Peoples R China

This research introduces a novel approach for detecting defects in concrete structures. It utilizes the gramian angular difference field (GADF) in combination with a Convolutional Neural Network (CNN) enhanced by depthwise separable convolutions and attention mechanisms. The key contribution of this work is the use of GADF to transform one-dimensional impact -echo signals into two-dimensional images, thereby improving feature extraction and computational efficiency for analysis by the CNN. This advancement offers a new perspective in non-destructive testing technologies for concrete infrastructure. Comprehensive evaluation on a varied dataset of concrete structural defects reveals that our GADF-CNN model achieves an impressive test accuracy of 98.24 %, surpassing conventional models like VGG16, ResNet18, DenseNet, and ResNeXt50, and excelling in precision, recall, and F1 -score metrics. Ultimately, this study enhances the integration of sophisticated image transformation techniques with deep learning, contributing to safer and more durable concrete infrastructure, and represents a noteworthy development in the field.

关键词： concrete structures defect detection gramian angular difference field convolutional neural networks non-destructive testing

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Soft Fault Diagnosis of Analog Circuits Based on Classification of GAF_RP Images With ResNet

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第10期42卷 5761-5782页

作者： Tang, Xuanzhong Zhou, Xin Liang, Wenhai Sichuan Normal Univ Coll Phys & Elect Engn 1819SectChenglong Ave Chengdu 610101 Sichuan Peoples R China Sichuan Normal Univ Key Lab Wireless Sensor Networks 1819Sect 2Chenglong Ave Chengdu 610101 Sichuan Peoples R China

Analog circuit fault diagnosis is widely used to ensure normal operation and fault location electronic equipment. In this study, a new method for fault diagnosis of analog circuits based on classification of gramian angular summation field and recurrence plot (GAF_RP) images is presented. The proposed method converts the time-response sequence signals of the analog circuit into a GAF_RP image through the gramian angular summation field, the gramian angular difference field and the recurrence plot methods. Therefore, more feature information about the time-response sequence signals can be obtained. And the bilinear interpolation method is used to compress the image, which improves the efficiency of neural network training and testing. Finally, an 18-layer residual neural network (ResNet-18) is used to perform feature extraction and learning on the GAF_RP images, to achieve accurate soft fault diagnosis of analog circuits. The simulation and actual experiment verify that this method can realize accurate and reliable soft fault diagnosis of the analog circuit.

关键词： Soft fault diagnosis Residual neural networks gramian angular Summation field gramian angular difference field

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A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 1982-1991页

作者： Wang, Zenghui Zhang, Jun Xia, Yi Chen, Peng Wang, Bing Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applic Sch Internet Hefei 230601 Peoples R China Anhui Univ Inst Phys Sci Hefei 230601 Peoples R China Anhui Univ Inst Informat Technol Hefei 230601 Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China

Functional near-infrared spectroscopy (fNIRS), a non-invasive optical technique, is widely used to monitor brain activities for disease diagnosis and brain-computer interfaces (BCIs). Deep learning-based fNIRS classification faces three major barriers: limited datasets, confusing evaluation criteria, and domain barriers. We apply more appropriate evaluation methods to three open-access datasets to solve the first two barriers. For domain barriers, we propose a general and scalable vision fNIRS framework that converts multi-channel fNIRS signals into multi-channel virtual images using the gramian angular difference field (GADF). We use the framework to train state-of-the-art visual models from computer vision (CV) within a few minutes, and the classification performance is competitive with the latest fNIRS models. In cross-validation experiments, visual models achieve the highest average classification results of 78.68% and 73.92% on mental arithmetic and word generation tasks, respectively. Although visual models are slightly lower than the fNIRS models on unilateral finger- and foot-tapping tasks, the F1-score and kappa coefficient indicate that these differences are insignificant in subject-independent experiments. Furthermore, we study fNIRS signal representations and the classification performance of sequence-to-image methods. We hope to introduce rich achievements from the CV domain to improve fNIRS classification research.

关键词： Functional near-infrared spectroscopy Brain modeling Transformers Visualization Task analysis Deep learning Support vector machines Functional near-infrared spectroscopy (fNIRS) brain-computer interfaces (BCIs) classification deep learning gramian angular difference field

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Cross-device target migration intelligent diagnosis method of wind power gearbox faults

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第2期35卷 025025-025025页

作者： Guo, Panpan Cui, Ben Zhang, Wenbin Kunming Univ Sci & Technol Coll Mech & Elect Engn Kunming 650500 Peoples R China China Huadian Corp Ltd Tianjin Junliangcheng Generat Co Ltd Tianjin 300300 Peoples R China Kunming Univ Coll Mech & Elect Engn Kunming 650214 Peoples R China Kunming Univ Shen Weiming Academician Workstn Intelligent Mfg T Kunming 650214 Peoples R China

The current convolutional neural network (CNN) deep learning algorithm for wind power gearbox fault diagnosis requires a substantial amount of high-quality tagged data from testbeds to achieve accurate results. However, it may not perform well in practical engineering scenarios where there is limited fault tag data and incomplete fault information. To overcome this challenge, a novel approach utilizing cross-device target transfer for intelligent diagnosis of wind turbine gearbox faults is proposed. This method transforms the fault diagnosis of wind turbine gearboxes into an image recognition and classification task. Initially, the source domain gearbox testbed data is subjected to dimension transformation using the gramian angular difference field technique, resulting in the generation of small sample two-dimensional images. This transformation simulates the limited fault tag data scenario encountered in practical engineering. Once the fault features have been augmented, the small sample source domain gearbox testbed data is fed into the IConvNeXt deep CNNs. These CNNs are interconnected and specifically designed for the field. They aim to extract and recognize fault features, thereby creating a pre-training model. The pre-training model is then transferred to the target domain wind power gearbox data for fault diagnosis experiments. The results of these experiments demonstrate the significant effectiveness of the proposed method in recognizing faults in small sample wind power gearboxes within the target domain. Overall, this method successfully achieves the goal of intelligent fault diagnosis through target transfer. It addresses the challenge of limited fault tag data and incomplete fault information, thereby enhancing the performance of CNNs in practical engineering scenarios.

关键词： wind turbine gearbox target transfer intelligent diagnosis small sample gramian angular difference field deep convolutional neural network

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