Information Fusion Research Articles

Metal surface crack depth laser acoustic emission detection method based on multivariate feature adaptive extraction and cross-modal interaction fusion

Surface crack depth detection of metal structures is of great significance to ensure the safe of equipment. However, the commonly used detection methods currently suffer from sensitivity and imaging dimension limitations, which make it difficult to ensure accuracy and feature extraction. To this end, a laser acoustic emission crack depth detection method that combines modal decomposition and deep learning is proposed. The new method is more sensitive to elastic wave changes that detect the depth of microscopic cracks and shows higher accuracy than traditional detection methods. At the same time, frequency-adaptive feature mode decomposition of multivariate signals and color multimodal feature fusion are used to solve the difficulty of time–frequency domain feature mining in current research. The crack depth detection network CDDNet is constructed using an attention-based fusion backbone, a novel cross-modal interaction fusion, and a hierarchical information fusion strategy. Its transfer learning achieves a crack detection accuracy level of 0.05 mm and outperforms other models in terms of accuracy, reaching 93.6 %. This paper shows that the proposed transfer learning laser acoustic emission technology and supporting data processing methods can effectively establish a connection between the experimental and simulation datasets, solving the current challenges of crack depth detection in terms of micro-precision and data processing.

A lightweight Mini-YOLOv5s algorithm for small target crack detection in tunnels

ABSTRACT The automatic detection and identification of tunnel cracks is very important for safe driving and tunnel maintenance. In order to accelerate the application of automated detection and reduce the deployment cost of target detection algorithm on edge devices, this paper adopts non-destructive testing and proposes a lightweight Mini-YOLOv5s algorithm for small target crack detection in tunnels, which mainly includes DP-C3 module, GDConv module, ECA-C3 module, CARAFE lightweight upsampling operator and loss function IGIoU. Among them, the DP-C3 module uses PConv to reduce the computational complexity of the model, and uses dilated convolution to enrich the receptive field to improve the information loss. The GDConv module improves the feature extraction capability of deep information. The feature fusion of small crack target information is realised by using ECA-C3 module and CARAFE lightweight up-sampling operator. A new loss function IGIoU is used to pay more attention to the contact degree of the boundary frame, so as to minimise the loss and improve the detection accuracy. Experiments show that the detection accuracy of Mini-YOLOv5s on self-made tunnel crack dataset reaches 91%, which compensates for the information loss, while the number of model parameters and GFLOPs are reduced by 35.4% and 30.4% respectively.

A noise-enhanced machine fault diagnosis method with multi-channel information fusion

ABSTRACT Condition health monitoring and fault diagnosis is able to safeguard against potential threats to machines. Nevertheless, the acquired signals by using a plenty of sensors are often accompanied by noise and meanwhile the useful information contained in the signals is not both complete and reliable, thereby together reducing the accuracy and stability of the fault diagnosis. Therefore, this paper proposes a noise-enhanced intelligent fault diagnosis method with multi-channel information fusion. Here, it would inject the varying levels of Gaussian noise into multi-channel input data as well as intelligent diagnostic models, with a view to elucidating the benefits of noise. Furthermore, the proposed method is validated through three experiments. The experimental results demonstrate that the diagnostic accuracy of the hydraulic motor and the two different bearings reaches 99.85%, 99.89%, and 99.85% by using the proposed method, respectively, which are increased by 2.50%, 4.09%, and 2.50%, respectively, compared with one without the injection of noise. In addition, by comparing the diagnostic accuracy of ones with single-channel and multi-channel information fusion under the optimal noise level in both cases, it is found that one with multi-channel information fusion shows an average of 3.20% increase than the single-channel one.

A compressor blade crack detection method based on the multilevel information fusion of acoustic and vibration signals

Single-signal crack detection methods perform unsatisfactorily in the presence of noise, prompting the need for alternative approaches. Leveraging multisource signals, which contain rich and complementary information, is a promising direction. In light of this, a compressor blade crack detection method based on the multilevel information fusion of acoustic and vibration signals is proposed. First, a multiscale data-level fusion convolutional neural network is designed, which fuses multisource homogeneous signals for crack detection. Second, multiple networks are trained with acoustic and vibration signals as inputs, respectively. Softmax layer of each network provides preliminary probabilities for each category, while the precision for each category in the validation set is calculated. Finally, an improved Dempster–Shafer theory approach is proposed to derive the final probability for each category according to the preliminary probabilities and precision, after which crack detection is realized. The proposed method is validated with experimental data from five categories of blades.

Bi-TAM-Net framework: fault diagnosis for insulated bearing based on new noise-resistant time-series framework

Abstract Insulated bearings are extensively employed in wind turbines and other applications as essential core parts of high-power frequency control motors. However, the influence of the wind turbine structure makes it difficult to define the wind turbine insulated bearing fault signal extraction, and there is no distinctive index or qualitative formula to describe the bearing fault. In order to solve the above challenges, Bi-TAM-Net framework is developed to diagnose the insulated bearing fault signals and achieve accurate identification of bearing faults. Firstly, the temporal information feature fusion model is created by the Bi-TAM-Net framework using the time-series bearing dataset as the model data input with recursive and chain linking rules in the direction of time-series evolution. By virtue of its parallel approach and deep stacking structure, it comprehensively portrays the bearing fault data and temporally fuses the global feature information, which can effectively improve the classification performance of insulated bearing fault data. Then the self-attention mechanism is introduced into the structure of the designed temporal information fusion model for optimization, which can be modeled in sequences of arbitrary length, extracting the correlation between the features, isolating the unimportant noise information, and strengthening the extraction ability of the proposed framework for important information. Finally, in order to verify the accuracy and superiority of the developed Bi-TAM-Net framework for bearing faults, based on the same dataset, the Bi-TAM-Net framework is compared and analyzed with seven methods such as the advanced TAM-Net model, and the results show that the Bi-TAM-Net framework has better superiority. This study offers the analytical approach with engineering application value for the design and maintenance of insulated bearings used in wind turbines.

Assembly method for curved porous circular workpieces based on multidimensional information fusion: Grid electrode component

To tackle the problems related to the complexities of obtaining depth information for circular workpiece with arc surface and holes, a novel vision guidance method has been introduced to precisely gauge interlayer spacing. The process includes the acquisition and preprocessing of point cloud depth information, along with matching and edge processing. The depth coordinates of the point cloud are subsequently determined. Two-dimensional image processing technology is employed to extract the coordinates of the center of the surface circular hole in the curved porous circular workpiece. The 3D coordinates are generated for guiding the assembly by integrating these datasets. Experiments on grid parts in aviation propulsion systems demonstrate that this method maintains dot matrix spacing within 0.5–1.2 mm, ensures the coaxiality of the center hole, and meets assembly standards. The results confirm the method’s effectiveness for automated assembly of curved porous round workpieces.

Graph-based stock prediction with multisource information and relational data fusion

With the application of multisource information in different fields, the combination of different types of information, such as numerical data and text information, has become a favourable choice for performing stock market analyses. Despite the rich information provided by multisource data, building structured relationships remains challenging. In addition, some market relationship-based analysis methods use a predefined graph structure as a stock relationship graph, which makes it impossible to sensitively aggregate attribute features, and these methods cannot dynamically update market relationships or relationship strengths. In this paper, we propose a novel dynamic attribute-driven graph attention network incorporating sentiment (AGATS) information, transaction data, and text data. Inspired by behavioural finance, we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through tensor fusion. In particular, real-time intramarket dependencies and key attribute information are captured with graph networks, enabling dynamic relationship and relationship strength updates. Experiments conducted on real datasets show that our model is capable of ourperforming previously developed methods in prediction and trading.

Research on Cattle Behavior Recognition and Multi-Object Tracking Algorithm Based on YOLO-BoT.

In smart ranch management, cattle behavior recognition and tracking play a crucial role in evaluating animal welfare. To address the issues of missed and false detections caused by inter-cow occlusions and infrastructure obstructions in the barn environment, this paper proposes a multi-object tracking method called YOLO-BoT. Built upon YOLOv8, the method first integrates dynamic convolution (DyConv) to enable adaptive weight adjustments, enhancing detection accuracy in complex environments. The C2f-iRMB structure is then employed to improve feature extraction efficiency, ensuring the capture of essential features even under occlusions or lighting variations. Additionally, the Adown downsampling module is incorporated to strengthen multi-scale information fusion, and a dynamic head (DyHead) is used to improve the robustness of detection boxes, ensuring precise identification of rapidly changing target positions. To further enhance tracking performance, DIoU distance calculation, confidence-based bounding box reclassification, and a virtual trajectory update mechanism are introduced, ensuring accurate matching under occlusion and minimizing identity switches. Experimental results demonstrate that YOLO-BoT achieves a mean average precision (mAP) of 91.7% in cattle detection, with precision and recall increased by 4.4% and 1%, respectively. Moreover, the proposed method improves higher order tracking accuracy (HOTA), multi-object tracking accuracy (MOTA), multi-object tracking precision (MOTP), and IDF1 by 4.4%, 7%, 1.7%, and 4.3%, respectively, while reducing the identity switch rate (IDS) by 30.9%. The tracker operates in real-time at an average speed of 31.2 fps, significantly enhancing multi-object tracking performance in complex scenarios and providing strong support for long-term behavior analysis and contactless automated monitoring.

Memorizing Swin-Transformer Denoising Network for Diffusion Model

Diffusion models have garnered significant attention in the field of image generation. However, existing denoising architectures, such as U-Net, face limitations in capturing the global context, while Vision Transformers (ViTs) may struggle with local receptive fields. To address these challenges, we propose a novel Swin-Transformer-based denoising network architecture that leverages the strengths of both U-Net and ViT. Moreover, our approach integrates the k-Nearest Neighbor (kNN) based memorizing attention module into the Swin-Transformer, enabling it to effectively harness crucial contextual information from feature maps and enhance its representational capacity. Finally, we introduce an innovative hierarchical time stream embedding scheme that optimizes the incorporation of temporal cues during the denoising process. This method surpasses basic approaches like simple addition or concatenation of fixed time embeddings, facilitating a more effective fusion of temporal information. Extensive experiments conducted on four benchmark datasets demonstrate the superior performance of our proposed model compared to U-Net and ViT as denoising networks. Our model outperforms baselines on the CRC-VAL-HE-7K and CelebA datasets, achieving improved FID scores of 14.39 and 4.96, respectively, and even surpassing DiT and UViT under our experiment setting. The Memorizing Swin-Transformer architecture, coupled with the hierarchical time stream embedding, sets a new state-of-the-art in denoising diffusion models for image generation.

Deep spatial-temporal information fusion dynamic graph convolutional network for traffic flow prediction

Abstract Predicting traffic flow is vital for advancing intelligent transportation systems, but achieving precise forecasts remains challenging. To deeply explore the complex and diverse dynamic spatio-temporal correlation of traffic data, this study proposes a multi-information fusion spatio-temporal dynamic graph convolution model for traffic flow prediction. In the model construction, firstly, a dynamic graph generation module is designed to fully capture the complex spatial correlation of the traffic network and model the changing spatio-temporal interaction; secondly, the spatial self-attention mechanism is used to dynamically focus on the spatial relationship between different nodes, and combined with the dynamic graph convolution network to extract the spatial dynamic correlation features of the road network to study deep spatiotemporal information; Finally, existing models rarely consider other traffic information related to traffic flow, and a spatiotemporal information fusion module is designed to enhance the ability of the model to perceive information, thereby improving the performance of traffic flow prediction models. Experiments are conducted on four real traffic datasets using three performance evaluation metrics and 18 baseline models. The results show that the model has higher prediction accuracy.

Thermal Error Transfer Prediction Modeling of Machine Tool Spindle with Self-Attention Mechanism-Based Feature Fusion

Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors. In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Firstly, the temperature information fusion features are built as inputs to the model, which is based on a self-attention mechanism to assign weights to the temperature information and fuse the features. Secondly, an improved direct normalization-based adaptive matrix approach is proposed, updating the background matrix using an autoencoder and reconstructing the adaptive matrix to realize domain self-adaptation. In addition, for the improved adaptive matrix, a criterion for determining whether the working conditions are transferrable to each other is proposed. The proposed method shows high prediction accuracy while ensuring training efficiency. Finally, thermal error experiments are performed on a VCM850 CNC machine tool.

A multi-source domain feature-decision dual fusion adversarial transfer network for cross-domain anti-noise mechanical fault diagnosis in sustainable city

Rotating machinery forms the critical backbone of infrastructure in a sustainable city, with bearings playing a pivotal role as key mechanical transmission components. Therefore, the health status of these bearings directly influences the safe operation of the infrastructure. Accurate and reliable diagnosis of defects in these components minimizes downtime, reduces maintenance costs, and prevents major accidents, ultimately providing insights in the construction and management of a sustainable city. Typically, in actual industrial scenarios, varying working conditions and various types of machines can result in significant discrepancies in the distribution of sample data. Moreover, the non-negligible noise may degrade the diagnostic performance. Therefore, realizing an accurate and reliable bearing diagnosis considering the cross-domain and noise environment remains a challenge. Leveraging the merits of information fusion and multi-source domain transfer learning, this article proposes a multi-source domain feature-decision dual fusion adversarial transfer network (DFATN) to break through the aforesaid limitations. Initially, an adversarial transfer framework is developed, incorporating novel feature matching evaluation and joint distribution difference losses. This framework is designed to facilitate the learning of feature invariants across domains and to enhance the sharing of domain-specific knowledge, even in noise. Relying on channel-spatial interactive feature fusion, a multi-scale feature extractor (MFE) is constructed to share the interaction and enhance the modeling of complex features in multiple dimensions. Additionally, a fault state-related decision fusion mechanism (SDF) is also implemented to integrate diagnostic information, significantly enhancing the generalization performance and robustness of the proposed network. By employing both public Paderborn University (PU) and laboratory-collected (Lab) datasets, the effectiveness and superiority of the proposed DFATN on bearing fault diagnosis are validated. For cross-working condition tasks, the proposed method realizes impressive performance, with average accuracies of 96.52% and 98.76% for Paderborn University (PU) and laboratory-collected (Lab) datasets, respectively. For cross-machine tasks, the average accuracy is 83.36%, outperforming other latest cross-domain fault diagnosis techniques.

Classification and recognition method of rice situation based on gray correlation degree

Grain storage is a complex process, affected by factors such as mold, temperature, humidity, and moisture. The use of multiple sensors to detect changes in rice pile parameters has gained prominence as a means to ensure the accuracy and timeliness of grain condition monitoring. However, the current technology does not effectively utilize data. The assessment criteria primarily rely on grain temperature, and the analysis of grain condition is simplistic. Additionally, it fails to adequately integrate information on temperature, humidity, moisture, gas concentration, and other parameters of the grain pile to form a unified assessment result. To address the isolated and one-sided reaction of various parameters in the grain pile, this thesis conducts research on the storage characteristics of heating, condensation, and mold condition. It combines the information fusion of temperature, humidity, moisture, and CO2 with normal grain conditions, constructs an assessment model based on the classification and identification of grain conditions under gray correlation, and achieves real-time dynamic assessment of the state of the grain pile. The experimental results show that the assessment model based on gray correlation can accurately discriminate between normal and mold conditions, but the accuracy in distinguishing heating and condensation still requires improvement. The overall recognition rate of the four types of grain conditions is 79%, which demonstrates the effectiveness of the model in identifying abnormal grain states.

A fault diagnosis method for bearings and gears in rotating machinery based on data fusion and transfer learning

Abstract Rotating machinery is a crucial component of industrial equipment, and the fault diagnosis of bearings and gears, as vital elements of rotating machinery, is essential since they often fail under harsh working conditions, leading to significant property losses and serious personal safety problems. However, fault data for gears and bearings are often sparse in actual condition, and it is a challenge to ensure the reliability and stability of fault diagnosis results by extracting the features of a single data. To solve the above problems, this paper proposes a fault diagnosis method that combines Transfer Learning and data fusion techniques. Firstly, in this method, two kinds of fault signals are transformed into Gramian Angular Difference Fields and Recurrence Plot. Next, a U-shaped feature fusion dual discriminator generative adversarial network is used to fuse two-dimensional images from multiple sensor data. Its feature fusion module deeply integrates the features of the two images, thereby solving the impact of single data on the reliability and stability of fault diagnosis. Moreover, open-source datasets are used for Transfer Learning training to tackle the small sample problem. Finally, a decision-level information fusion classifier, the Dual-Branch Dempster-Shafer Classifier (DB-DSC), classifies the fused images. This classifier incorporates an improved soft threshold function and D-S evidence theory to achieve adaptive gradient changes and improve the robustness and accuracy of classification results. The experimental results show the effectiveness and stability of the proposed method, and the generated images get high score in several metrics. The average classification accuracy of the classification network reaches 93% and 92.5% on the two datasets, Therefore, the proposed method exhibits strong fault diagnosis capabilities under the small sample conditions of bearings and gears.

Enhancing precision of flood estimation in EOS-04 SAR imagery: a statistical approach

Floods pose a recurrent and devastating natural disaster in India, and accurate and timely assessment of their extent is crucial for effective disaster management and mitigation efforts. In this regard, this study proposes a novel approach for flood inundation estimation through the statistical analysis of two key geospatial datasets: the Normalized Difference Flood Index (NDFI) and temporal Standard Deviation images. To establish a baseline under ordinary conditions, two products were derived. The first product is the mean image during the non-flood season, and the second is the temporal Standard Deviation image for the entire year. These images are derived from Sentinel-1A Synthetic Aperture Radar (SAR) data using Google Earth Engine (GEE). The NDFI is calculated by comparing the mean image to the image obtained during flood event, allowing the identification of flood-affected areas. The process involves constructing Kernel Density Estimation (KDE) plots for NDFI and Standard Deviation, from which a 95% density ellipse is generated using the covariance matrix and eigenvalues. Multiple SAR scenes from diverse regions in India are analyzed individually, yielding density ellipses for each location. The innovative step lies in synthesizing these individual ellipses into the best fit ellipse through the convex hull method. This best fit ellipse encapsulates the general flood characteristics observed across disparate geographic regions, providing a unified representation. The derived best fit ellipse serves as a powerful tool for generating near-real-time flood inundation maps, with an overall accuracy of over 98.2%. Thus, the fusion of statistical insights and historical SAR information enables rapid near-real-time flood mapping, with enhanced accuracy of flood extent predictions.

Optimization Algorithm of Network Node Layout of Fresh Agricultural Products’ Online Consumption Behavior in E-Commerce Environment

In order to improve the efficiency of online consumption and supply chain efficiency of fresh agricultural products in the e-commerce environment, a network node layout optimization algorithm was designed. In the e-commerce environment, a network node deployment model has been constructed for the online consumption behavior of fresh agricultural products. A network transmission supply chain detection model was established through the rotation scheduling method. By combining distributed transformation protocols, the spatial layout characteristics of network nodes have been reorganized. Further introduction of a multi-coverage scheduling model, combined with sensor information fusion tracking technology, accurately detects the layout characteristics of network nodes. Based on these detection data, forwarding and adaptive control protocols were developed. Real-time control using the node traversal method was developed. Under the optimal node supply chain transmission mechanism, the scheduling of the network node layout has been achieved. Meanwhile, convolutional neural networks were utilized to dynamically adjust layout control to ensure convergence and stability. The simulation results show that the node forwarding ability of the network node layout design for online consumption behavior of fresh agricultural products in the e-commerce environment is good, which improves the output balance of the network supply chain transmission of online consumption behavior of fresh agricultural products in the e-commerce environment. In addition, the optimized delivery cost decreased by 20% compared to before optimization, delivery efficiency increased by 30%, and inventory turnover rate increased by 25%, confirming that the proposed method has good optimization performance.

Information fusion in order-2 fuzzy environments: A matrix transformation perspective

Order-2 information granules, as a representation of multi-layer structured information, have recently rekindled discussions. It is usually generated by abstracting order-1 information granules. When the dependence relationship and corresponding membership function of the order-1 information granules (reference information granules) are known, Pedrycz et al. used a method based on gradient optimization to complete the aggregation of the order-2 information granules. In this paper, we discuss a more specific scenario: not only the dependencies between reference information granules are captured, but also the order-1 information granules can be expressed as specific fuzzy information distributions. For this, we propose a fusion scheme of order-2 fuzzy sets based on matrix transformation called CQRP. To our knowledge, it is the first method that completely integrates the structural information of the order-2 environment into the fusion of order-2 fuzzy sets. In the process, we also creatively proposed the attraction and exclusion of structural information, deepening the understanding of structural information. Through sufficient comparison and analysis, we prove that it makes fuller use of information in the order-2 environment and is more reasonable and effective in tasks such as classification and identification.

Multisource information fusion for safety risk assessment in complex projects considering dependence and uncertainty.

The success of tunneling projects is crucial for infrastructure development. However, the potential leakage risk is particularly challenging due to the inherent uncertainties and fuzziness involved. To address this demanding challenge, a hybrid approach integrating the copula theory, cloud model, and risk matrix, is proposed. The dependence of multiple risk-related influential factors is explored by the construct of the copula-cloud model, and the diverse information is fused by applying the risk matrix to gain a crisp risk result. A case study is performed to test the applicability of the proposed approach, in which a risk index system consisting of nine critical factors is developed and Sobol-enabled global sensitivity analysis (GSA) is incorporated to investigate the contributions of different factors to the risk magnitude. Key findings are as follows: (1) Risk statuses of the studied three tunnel sections are perceived as under grade I (safe), II (low-risk), and III (medium-risk), respectively, and the waterproof material aspect is found prone to deteriorating the tunnel sections. Furthermore, the proposed approach allows for a better understanding of the trends in the risk statuses of the tunnel sections. (2) Strong interactions between influential factors exist and exert impacts on the final risk results, proving the necessity of studying the factor dependence. (3) The developed neutral risk matrix presents a strong robustness and displays a higher recognition capacity in risk assessment. The novelty of this research lies in the consideration of the dependence and uncertainty in multisource information fusion with a hybrid copula-cloud model, enabling to perform a robust risk assessment under different risk matrices with varying degrees of risk tolerance.

Research on Multi-Sensor Fusion Fault Diagnosis Method Based on Spatiotemporal Attention Mechanism

As industrial systems become increasingly complex, real-time monitoring and intelligent management of industrial equipment have become imperative. However, the limitations in coverage and accuracy of single sensors make it challenging to comprehensively characterize the operational state of equipment, leading to reduced system reliability and increased pressures on data transmission and storage. To address these challenges, this study presents a novel fault diagnosis method based on multi-sensor fusion using a spatio-temporal attention mechanism. Initially, one-dimensional convolutional neural networks (1D-CNN) are employed to extract features from raw signals, effectively capturing local characteristics and ensuring the integrity and validity of fault signals. Subsequently, the spatiotemporal attention mechanism adjusts the feature weights based on the temporal and spatial correlations of different sensors, as well as their respective importance, thereby capturing the spatio-temporal dependencies across multiple sensors and enhancing the efficacy of information fusion. Finally, the proposed method is validated through experiments on a nickel flash smelting furnace system. The results demonstrate that the method achieves a fault diagnosis accuracy exceeding 97.78%, significantly enhancing fault detection and decision-making performance.

Application and Research of Intelligent Multi-source Information Fusion Technology in Firefighting Equipment

As modern firefighting environments become increasingly complex, traditional firefighter equipment and operational methods are no longer adequate to address the high-risk challenges of fire rescue operations. Fire scenes are characterized by complex and hazardous environmental factors, with firefighters facing multiple threats such as extreme temperatures, dense smoke, and toxic gases. Therefore, ensuring the safety and efficiency of firefighters is of paramount importance. This study integrates multiple sensor technologies to enable real-time monitoring of firefighters' physiological status, posture changes, and fire scene conditions. The system incorporates ErgoLAB wireless ECG and PPG sensors, the BWT61CL posture sensor, and the MAG14mini infrared camera, which are capable of simultaneously capturing key physiological data and heat distribution in the fire scene. To ensure data accuracy and real-time performance, the study employs efficient signal preprocessing techniques, including noise reduction, baseline correction, and time synchronization. In addition, wireless transmission technology and multimodal data fusion algorithms are utilized to comprehensively analyze the firefighters' status and fire scene conditions. This approach significantly enhances the precision of firefighter safety monitoring and operational efficiency, demonstrating substantial innovation and practical value.