Multi-scale Feature Extraction Module Research Articles

Transformer assisted by DSC and BiLSTM for bearing fault pattern recognition under strong noise interference

ABSTRACT Deep learning has greatly promoted intelligent fault diagnosis for bearings and has led to the development of many intelligent fault recognition methods based on deep neural networks. However, bearing fault signals in industrial applications are susceptible to noise interference, and intelligent recognition methods for bearing faults under strong noise interference are lacking. An intelligent fault pattern recognition model termed IFPR-MDL, which is based on a Transformer assisted by depthwise separable convolutional (DSC) and bidirectional long short-term memory (BiLSTM) networks, is designed to identify bearing faults under strong noise conditions. In this model, multiscale features are first extracted by a multiscale feature extraction module designed on the basis of DSC; then, contextual information is further extracted by a BiLSTM, and information from different scales is integrated by Transformer encoders. The DSC network facilitates model parameter reduction, the BiLSTM network has expertise in mining temporal features, and the Transformer network excels at capturing long-range feature correlations. This model fully utilises the advantages of these models and complements them to improve the anti-interference ability of bearing fault pattern recognition. Tests on several public datasets show that the proposed model has higher recognition accuracy, stronger generalisation ability, and noise resistance in strong noise situations.

Multiple myeloma segmentation net (MMNet): an encoder-decoder-based deep multiscale feature fusion model for multiple myeloma segmentation in magnetic resonance imaging.

Patients with multiple myeloma (MM), a malignant disease involving bone marrow plasma cells, shows significant susceptibility to bone degradation, impairing normal hematopoietic function. The accurate and effective segmentation of MM lesion areas is crucial for the early detection and diagnosis of myeloma. However, the presence of complex shape variations, boundary ambiguities, and multiscale lesion areas, ranging from punctate lesions to extensive bone damage, presents a formidable challenge in achieving precise segmentation. This study thus aimed to develop a more accurate and robust segmentation method for MM lesions by extracting rich multiscale features. In this paper, we propose a novel, multiscale feature fusion encoding-decoding model architecture specifically designed for MM segmentation. In the encoding stage, our proposed multiscale feature extraction module, dilated dense connected net (DCNet), is employed to systematically extract multiscale features, thereby augmenting the model's sensing field. In the decoding stage, we propose the CBAM-atrous spatial pyramid pooling (CASPP) module to enhance the extraction of multiscale features, enabling the model to dynamically prioritize both channel and spatial information. Subsequently, these features are concatenated with the final output feature map to optimize segmentation outcomes. At the feature fusion bottleneck layer, we incorporate the dynamic feature fusion (DyCat) module into the skip connection to dynamically adjust feature extraction parameters and fusion processes. We assessed the efficacy of our approach using a proprietary dataset of MM, yielding notable advancements. Our dataset comprised 753 magnetic resonance imaging (MRI) two-dimensional (2D) slice images of the spinal regions from 45 patients with MM, along with their corresponding ground truth labels. These images were primarily obtained from three sequences: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and short tau inversion recovery (STIR). Using image augmentation techniques, we expanded the dataset to 3,000 images, which were employed for both model training and prediction. Among these, 2,400 images were allocated for training purposes, while 600 images were reserved for validation and testing. Our method showed increase in the intersection over union (IoU) and Dice coefficients by 7.9 and 6.7 percentage points, respectively, as compared to the baseline model. Furthermore, we performed comparisons with alternative image segmentation methodologies, which confirmed the sophistication and efficacy of our proposed model. Our proposed multiple myeloma segmentation net (MMNet), can effectively extract multiscale features from images and enhance the correlation between channel and spatial information. Furthermore, a systematic evaluation of the proposed network architecture was conducted on a self-constructed, limited dataset. This endeavor holds promise for offering valuable insights into the development of algorithms for future clinical applications.

Grape clusters detection based on multi-scale feature fusion and augmentation

This paper addresses the challenge of low detection accuracy of grape clusters caused by scale differences, illumination changes, and occlusion in realistic and complex scenes. We propose a multi-scale feature fusion and augmentation YOLOv7 network to enhance the detection accuracy of grape clusters across variable environments. First, we design a Multi-Scale Feature Extraction Module (MSFEM) to enhance feature extraction for small-scale targets. Second, we propose the Receptive Field Augmentation Module (RFAM), which uses dilated convolution to expand the receptive field and enhance the detection accuracy for objects of various scales. Third, we present the Spatial Pyramid Pooling Cross Stage Partial Concatenation Faster (SPPCSPCF) module to fuse multi-scale features, improving accuracy and speeding up model training. Finally, we integrate the Residual Global Attention Mechanism (ResGAM) into the network to better focus on crucial regions and features. Experimental results show that our proposed method achieves a mAP0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{0.5}$$\\end{document} of 93.29% on the GrappoliV2 dataset, an improvement of 5.39% over YOLOv7. Additionally, our method increases Precision, Recall, and F1 score by 2.83%, 3.49%, and 0.07, respectively. Compared to state-of-the-art detection methods, our approach demonstrates superior detection performance and adaptability to various environments for detecting grape clusters.

White blood cell classification network using MobileNetv2 with multiscale feature extraction module and attention mechanism

White blood cells play a crucial role in the human immune system. The accurate classification of white blood cells can help doctors diagnose various diseases for patients. To enhance the classification accuracy of white blood cells micro-vision images, an efficient lightweight deep learning network called ICAFF-MobileNetv2 is proposed in this paper. Firstly, the pruning operations are applied to the MobileNetv2 and the multiscale feature extraction module is proposed to reduce the model size while ensuring the classification accuracy. Secondly, the improved coordinate attention mechanism module and the attention feature fusion module are incorporated into the inverted residual structures to enhance the feature extraction capability and the classification accuracy of the network. Furthermore, considering the imbalance among different types of white blood cells in the datasets, the method of label smoothing is introduced to improve the cross-entropy loss function and further enhance the classification accuracy. Finally, the improved network is validated on the proposed mixed dataset. The experimental results demonstrate that the proposed network features high classification accuracy and small model size. Compared to other classification networks, ICAFF-MobileNetv2 exhibits superior performance with 98.54 %, 98.21 %, 98.56 %, and 98.38 % of the classification accuracy, recall, precision and F1 score respectively, which are 0.7 %, 0.86 %, 0.92 %, and 0.89 % better than MobileNetv2.

MFPIDet: improved YOLOV7 architecture based on multi-scale feature fusion for prohibited item detection in complex environment

Prohibited item detection is crucial for the safety of public places. Deep learning, one of the mainstream methods in prohibited item detection tasks, has shown superior performance far beyond traditional prohibited item detection methods. However, most neural network architectures in deep learning still lack sufficient local feature representation ability for overlapping and small targets, and ignore the problem of semantic conflicts caused by direct feature fusion. In this paper, we propose MFPIDet, a novel prohibited item detection neural network architecture based on improved YOLOV7 to achieve reliable prohibited item detection in complex environments. Specifically, a multi-scale attention module (MAM) backbone is proposed to filter the redundant information of target regions and further applied to enhance the local feature representation ability of overlapping objects. Here, to reduce the redundant information of target regions, a squeeze-excitation (SE) block is used to filter the background. Then, aiming at enhancing the feature expression ability of overlapping objects, a multi-scale feature extraction module (MFEM) is designed for local feature representation. In addition, to obtain richer context information, We design an adaptive fusion feature pyramid network (AF-FPN) to combine the adaptive context information fusion module (ACIFM) with the feature fusion module (FFM) to improve the neck structure of YOLOV7. The proposed method is validated on the PIDray dataset, and the tested results showed that our method obtained the highest mAP (68.7%), which is improved by 3.5% than YOLOV7 methods. Our approach provides a new design pattern for prohibited item detection in complex environments and shows the development potential of deep learning in related fields.

Low-light image enhancement using generative adversarial networks

In low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images.

Attention-enhanced dilated convolution for Parkinson’s disease detection using transcranial sonography

BackgroundTranscranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.MethodsThis study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.ResultsThe study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.ConclusionThe AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.Graphical

JL-TFMSFNet: A domestic cat sound emotion recognition method based on jointly learning the time–frequency domain and multi-scale features

There is a close biological relationship between cats and humans. Emotion recognition of domestic cat meows plays an essential role in human–animal relationships, animal–environment relationships, and animal conservation. This study proposed a deep learning network named JL-TFMSFNet. It jointly learns the time–frequency domain information and multi-scale features to recognize the sound emotions of domestic cats. Firstly, the Mel feature of the domestic cat’s sound was extracted based on the Mel filter, using the proposed Multi-scale Feature Extraction Module (MFEM) to extract weighting information to generate a deeper one. Secondly, the semantic information in the spectrogram was learned from the time–frequency domain using the Time–Frequency Attention Mechanism (TFAM) proposed in the backbone network. Then, the Diverse Branch Block (DBB) module with the different receptive fields was added to improve the recognition performance. Finally, validation experiments were conducted on the self-built dataset Cat_Emotion_Sound (CES) nd the public dataset Urbansound8K. The experiments demonstrated that JL-TFMSFNet outperformed the state-of-the-art sound classification models. The average accuracies of the proposed model in this paper on these two datasets are 94.43% and 96.14%, and the F1-score are 94.01% and 95.65%.

LithoSegNet: Regional attention-based deep fusion of multi-scale and cross-stage features for real-time lithology segmentation

Lithology identification plays an important role in engineering construction, disaster prediction, reservoir evaluation, and other fields. However, with the development of mechanization and automation, traditional lithology recognition methods are gradually unable to meet existing demands in terms of recognition speed and efficiency. In order to achieve real-time, fast, and accurate identification of lithology, we designed a lightweight model backbone, proposed a multi-scale feature extraction module and a cross-stage feature fusion module based on regional attention, developed a step-wise fusion scheme for multi-stage feature, and developed a real-time lithology segmentation model. The experimental results show that the feature extraction module improves the mIoU of the model by 0.0021; The cross-stage feature fusion module improves the mIoU of the model by 0.0104; The step-wise fusion scheme can effectively fuse shallow spatial features and deep semantic features. The model mIoU using this scheme is 0.0246 and 0.0145 higher than that using SegFormer and SegNeXt fusion schemes, respectively. Our model LithoSegNet can achieve a mIoU of 0.9583 with a speed of 116.25 images per second, comprehensively surpassing many state-of-the-art models. The research results can provide technical support for automated geological sketching in field exploration and on-site construction, and lay the foundation for intelligent construction of underground engineering, which has important scientific and engineering application value.

Fault classification of photovoltaic module infrared images based on transfer learning and interpretable convolutional neural network

In the operation of photovoltaic (PV) power plants, infrared cameras are commonly utilized for monitoring the operational status of PV modules. This study focuses on the performance improvement and complexity reduction of convolutional neural network (CNN) when used for fault classification based on infrared images of PV module. By implementing the transfer learning strategy on some famous CNN models, it is observed that the number of convolutional layers has weak impact on the classification results. Therefore, a transfer-learning-based depth reduction approach for CNN models (TLDR-CNN approach) is proposed, and the VGG16 model is employed for verification. Then, a multi-scale feature extraction module (MSFE module) is developed for efficiently replacing the convolutional layers to reduce model complexity and improve classification performance, and several representative model configurations are employed for convolutional layer replacement. Experimental results demonstrate that the application of the developed MSFE module significantly outperforms the baseline model on both classification performance and model complexity. Specifically, the modified model with a reduction of 5 convolutional layers exhibits notable improvements over the training results, with an accuracy increase of 0.90%, precision increase of 0.98%, F1 score increase of 6.89%, and a Matthews correlation coefficient increase of 1.01%. Finally, the interpretability of the above outperformance is also provided by using the Grad-CAM method. The generated CAM images show that the modified model concentrates its weights more on the regions crucial for the model to learn, so the features can be extracted more efficiently.

Highly Accurate and Lightweight Detection Model of Apple Leaf Diseases Based on YOLO

To mitigate problems concerning small-sized spots on apple leaves and the difficulties associated with the accurate detection of spot targets exacerbated by the complex backgrounds of orchards, this research used alternaria leaf spots, rust, brown spots, gray spots, and frog eye leaf spots on apple leaves as the research object and proposed the use of a high-accuracy detection model YOLOv5-Res (YOLOv5-Resblock) and lightweight detection model YOLOv5-Res4 (YOLOv5-Resblock-C4). Firstly, a multiscale feature extraction module, ResBlock (residual block), was designed by combining the Inception multi-branch structure and ResNet residual idea. Secondly, a lightweight feature fusion module C4 (CSP Bottleneck with four convolutions) was designed to reduce the number of model parameters while improving the detection ability of small targets. Finally, a parameter-streamlining strategy based on an optimized model architecture was proposed. The experimental results show that the performance of the YOLOv5-Res model and YOLOv5-Res4 model is significantly improved, with the mAP0.5 values increasing by 2.8% and 2.2% compared to the YOLOv5s model and YOLOv5n model, respectively. The sizes of the YOLOv5-Res model and YOLOv5-Res4 model are only 10.8 MB and 2.4 MB, and the model parameter counts are reduced by 22% and 38.3% compared to the YOLOv5s model and YOLOv5n model.

Face photo-drawing conversion based on multi-scale feature-enhanced generative adversarial networks

ABSTRACT This paper introduces a novel face photo-to-sketch synthesis method using a multi-scale feature-enhanced generative adversarial network (MFEGAN). The MFEGAN framework captures features at various scales through a multi-scale feature extraction module, enhanced by an attention mechanism. An improved attention residual block in the generator adaptively refines deep image features, improving overall quality. A pre-trained feature extraction network extracts and fuses face-specific features, enriching identity information. Multi-scale perceptual and focal frequency losses optimize detail quality, aligning with human perception. Experimental results show that MFEGAN outperforms existing methods in visual appeal and fidelity to original identity features.

High Efficiency Deep-learning Based Video Compression

Although deep learning technique has achieved significant improvement on image compression, but its advantages are not fully explored in video compression, which leads to the performance of deep-learning-based video compression (DLVC) is obviously inferior to that of hybrid video coding framework. In this article, we proposed a novel network to improve the performance of DLVC from its most important modules, including Motion Process (MP), Residual Compression (RC), and Frame Reconstruction (FR). In MP, we design a split second-order attention and multi-scale feature extraction module to fully remove the warping artifacts from multi-scale feature space and pixel space, which can help reduce the distortion in the following process. In RC, we propose a channel selection mechanism to gradually drop redundant information while preserving informative channels for a better rate-distortion performance. Finally, in FR, we introduce a residual multi-scale recurrent network to improve the quality of the current reconstructed frame by progressively exploiting temporal context information between it and its several previous reconstructed frames. Extensive experiments are conducted on the three widely used video compression datasets (HEVC, UVG, and MCL-JVC), and the performance demonstrates the superiority of our proposed approach over the state-of-the-art methods.

Vessel Segmentation in Fundus Images with Multi-Scale Feature Extraction and Disentangled Representation

Vessel segmentation in fundus images is crucial for diagnosing eye diseases. The rapid development of deep learning has greatly improved segmentation accuracy. However, the scale of the retinal blood-vessel structure varies greatly, and there is a lot of noise unrelated to blood-vessel segmentation in fundus images, which increases the complexity and difficulty of the segmentation algorithm. Comprehensive consideration of factors like scale variation and noise suppression is imperative to enhance segmentation accuracy and stability. Therefore, we propose a retinal vessel segmentation method based on multi-scale feature extraction and decoupled representation. Specifically, we design a multi-scale feature extraction module at the skip connections, utilizing dilated convolutions to capture multi-scale features and further emphasizing crucial information through channel attention modules. Additionally, to separate useful spatial information from redundant information and enhance segmentation performance, we introduce an image reconstruction branch to assist in the segmentation task. The specific approach involves using a disentangled representation method to decouple the image into content and style, utilizing the content part for segmentation tasks. We conducted experiments on the DRIVE, STARE, and CHASE_DB1 datasets, and the results showed that our method outperformed others, achieving the highest accuracy across all three datasets (DRIVE:0.9690, CHASE_DB1:0.9757, and STARE:0.9765).

Boundary-enhanced dual-stream network for semantic segmentation of high-resolution remote sensing images

ABSTRACT Deep convolutional neural networks (DCNNs) have been successfully used in semantic segmentation of high-resolution remote sensing images (HRSIs). However, this task still suffers from intra-class inconsistency and boundary blur due to high intra-class heterogeneity and inter-class homogeneity, considerable scale variance, and spatial information loss in conventional DCNN-based methods. Therefore, a novel boundary-enhanced dual-stream network (BEDSN) is proposed, in which an edge detection branch stream (EDBS) with a composite loss function is introduced to compensate for boundary loss in semantic segmentation branch stream (SSBS). EDBS and SSBS are integrated by highly coupled encoder and feature extractor. A lightweight multilevel information fusion module guided by channel attention mechanism is designed to reuse intermediate boundary information effectively. For aggregating multiscale contextual information, SSBS is enhanced by multiscale feature extraction module and hybrid atrous convolution module. Extensive experiments have been tested on ISPRS Vaihingen and Potsdam datasets. Results show that BEDSN can achieve significant improvements in intra-class consistency and boundary refinement. Compared with 11 state-of-the-art methods, BEDSN exhibits higher-level performance in both quantitative and visual assessments with low model complexity. The code will be available at https://github.com/lixinghua5540/BEDSN.

Densely integrated multi-branch attentive net for image dehazing

Single image dehazing is a fundamental but challenging task in image processing. Various deep learning-based methods have achieved great dehazing performance. However, there are still hazy residues, even color distortion and texture loss when removing haze from complex outdoor images in dense hazy scenes. A densely integrated multi-branch attentive net for image dehazing is proposed in the paper to address the above problems. The network includes a multi-scale feature extraction module and a dense-feature fusion module. The multi-scale feature extraction module adopts a multi-branch structure composed of residual channel attention blocks, which can expand the receptive field and filter the extracted features of diverse scales by weighting for fusion. It raises network learning accuracy. The dense-feature fusion module contains a multi-level feature fusion module for front and back layers, a color information renovation module, and a feature enhancement module. It achieves dynamically adjusting the channel weights of features at diverse scales, learning rich context information and suppressing redundant information, and compensating for the lack of color and texture information. The dense-feature fusion module bolsters the generalization capability of the network. The proposed method achieves superior objective and subjective evaluation results via quantitative and qualitative experiments on synthetic hazy images and natural hazy images, with better generalization ability and dehazing effect than the current SOTA dehazing algorithms, and effectively ameliorates the color distortion and incomplete dehazing.

Smartphone-based straw incorporation: An improved convolutional neural network

In conservation tillage, the effective incorporation of straw into soil is a critical factor for optimizing decomposition rates and addressing challenges related to straw accumulation. Consequently, this study introduces an innovative straw semantic segmentation model to extract straw mixed with soil from images, and proposes a method to measure the quality of straw and soil mixing based on this model. We conducted field experiments based on this method, analyzed the quality of straw incorporation at different depths, and finally integrated the model into a mobile application to facilitate the automatic, accurate, and rapid assessment of the quality of straw and soil mixing. The methodology of this study as an improved convolutional neural network entailed the fusion of deep separable convolution with a multi-scale feature extraction module, utilizing convolution kernels of varying sizes to capture the diverse features of straw. Further refinement included halving the first convolution channel and implementing a direct connection structure. Attention gates were strategically deployed to enhance salient features, resulting in superior accuracy compared to the original U-shaped network, fully convolutional network, and Otsu algorithm. Notably, the proposed model achieved enhanced performance while reducing the number of parameters and model complexity to one-third and one-half of the original U-shaped network, respectively. For a quantitative description of the rate and uniformity of straw incorporation, this study employed image processing technology and a grid counting method. The synergistic integration of smartphone imagery and deep learning expedited the delivery of rapid and reliable results, promising practical applicability in future straw incorporation practices. A demonstration video of the application is accessible at: https://doi.org/10.6084/m9.figshare.14480745.v3. The findings of this research contribute significantly to the advancement of conservation tillage by establishing a robust framework for the assessment and enhancement of straw incorporation operations’ efficiency.

SCPMan: Shape context and prior constrained multi-scale attention network for pancreatic segmentation

Due to the poor prognosis of Pancreatic cancer, accurate early detection and segmentation are critical for improving treatment outcomes. However, pancreatic segmentation is challenged by blurred boundaries, high shape variability, and class imbalance. To tackle these problems, we propose a multiscale attention network with shape context and prior constraint for robust pancreas segmentation. Specifically, we proposed a Multi-scale Feature Extraction Module (MFE) and a Mixed-scale Attention Integration Module (MAI) to address unclear pancreas boundaries. Furthermore, a Shape Context Memory (SCM) module is introduced to jointly model semantics across scales and pancreatic shape. Active Shape Model (ASM) is further used to model the shape priors. Experiments on NIH and MSD datasets demonstrate the efficiency of our model, which improves the state-of-the-art Dice Score for 1.01% and 1.03% respectively. Our architecture provides robust segmentation performance, against the blurry boundaries, and variations in scale and shape of pancreas.

A multi-step loss meta-learning method based on multi-scale feature extraction for few-shot fault diagnosis

Existing deep learning (DL) algorithms are based on a large amount of training data and they face challenges in effectively extracting fault features when dealing with few-shot fault diagnoses. Model-agnostic meta-learning (MAML) also faces some challenges, including the limited capability of the basic convolutional neural network (CNN) with a single convolutional kernel to extract fault features comprehensively, as well as the instability of model training due to the inner and outer double-layer loops. To address these issues, this paper presents a multi-step loss meta-learning method based on multi-scale feature extraction (MFEML). Firstly, an improved multi-scale feature extraction module (IMFEM) is designed to solve the problem of the insufficient feature extraction capability of the CNN. Secondly, the multi-step loss is used to reconstruct the meta-loss to address the issue of MAML training instability. Finally, the experimental results of two datasets demonstrate the effectiveness of the MFEML.

Fault Diagnosis of Rotating Machinery Bearings Based on Multi-Scale Attention Feature Fusion under Few Shot and Complex Working Conditions

Accidents caused by failures in rotating machinery bearings have heightened attention in bearing failure diagnosis. Scholars have explored methods to build fault diagnosis models to tackle the challenge of creating diagnostic models with few bearing failure samples available. However, many diagnostic models may suffer from overfitting issues under insufficient samples, affecting fault diagnosis performance. Additionally, rotating machinery operates under changing, complex conditions and noise interference, further deteriorating the effectiveness of fault diagnosis. This paper proposes a fault diagnosis model based on multi-scale attentional feature fusion (FD-MSAFF) to address the issue above. This model comprises a multi-scale feature extraction module, an attentional feature fusion module, and an MMD-based weighted prototype network. The FD-MSAFF model improves few-shot learning by using its multi-scale feature extraction and depth-wise separable attention modules to blend multi-scale features effectively with contextual information. It also tackles classification issues in few-shot sizes with its MMD-weighted prototype network, which is less susceptible to noise. Simulated tests under complex scenarios, such as changing conditions, noise variations, cross-bearing conditions, and comparisons with other algorithms, have proven the FD-MSAFF model's superior accuracy and generalization in diagnosing bearing faults in rotating machinery.