Angular Field Research Articles

A Rolling Bearing Fault Diagnosis Method Combining MSSSA-VMD with the Parallel Network of GASF-CNN and BiLSTM

Once the rolling bearing fails, it will threaten the normal operation of the whole rotating machinery. Therefore, it is very necessary to conduct research on rolling bearing fault diagnosis. This paper proposes a rolling bearing fault diagnosis method combining MSSSA-VMD (variational mode decomposition optimized by the improved salp swarm algorithm based on mixed strategy) with the parallel network of GASF-CNN (convolutional neural network based on Gramian angular summation field) and bi-directional long short-term memory (BiLSTM) to solve the problem of poor diagnostic performance for the rolling bearing faults caused by the respective limitations of existing fault diagnosis methods based on signal processing and deep learning. Firstly, MSSSA-VMD is proposed to solve the problem where the decomposition effect of VMD is not ideal due to improper parameter selection. Then, MSSSA-VMD is employed to preprocess and extract characteristics. Finally, the extracted characteristics are input into the parallel network of GASF-CNN and BiLSTM for diagnosis. In one channel of the parallel network, GASF is used to convert the characteristic vectors into a two-dimensional image, which is then fed into CNN for spatial characteristic extraction. In the other channel of the parallel network, the characteristic vectors are directly input into BiLSTM for temporal characteristic extraction. Experimental results demonstrate that the proposed method has good performance in terms of fault diagnosis performance under constant operating conditions, generalization ability under variable operating conditions and noise resistance.

Breaking dimensional barriers in hyperspectral target detection: Atrous convolution with Gramian Angular field representations

Hyperspectral images contain extensive spectral bands with rich spectral information that reflects object properties. Leveraging state-of-the-art deep learning techniques has proven to be effective in hyperspectral target detection. However, compared to two-dimensional matrix data, the one-dimensional nature of spectral sequence limits the information that can be extracted, posing a challenge for deep learning-based hyperspectral target detection methodologies. To address this issue, a novel hyperspectral target detection method employing atrous convolution with gramian angular field representations is proposed in this paper. This approach breaks the barrier between one-dimensional vector and two-dimensional matrix by gramian angular field, transforming the spectral sequences from one-dimensional vectors into two-dimensional matrices, enabling the exploration of multidimensional relationships within spectral band relations through an atrous convolution-based spectral feature extraction network. The proposed model transcends the traditional one-dimensional spectral target detection limitations, offering a new perspective for spectral-based hyperspectral target detection. Experimental results on four real-world hyperspectral datasets demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in detection performance, showcasing its potential for advancing hyperspectral target detection.

Reconstructing angular light field by learning spatial features from quadrilateral epipolar geometry

Recent research on dense multi-view image reconstruction has attracted considerable attention, due to its enhancement of applications such as 3D reconstruction, de-occlusion, depth sensing, saliency detection, and prominent object identification. This paper introduces a method for reconstructing high-density light field images, addressing the challenge of balancing angular and spatial resolution within the constraints of sensor resolution. We propose a three-stage network architecture for LF reconstruction that processes dense epipolar, spatial, and angular information efficiently. Our network processes epipolar information in the first stage, spatial information in the second stage, and angular information in the third stage. By extracting quadrilateral epipolar features from multiple directions, our model constructs a robust feature hierarchy for accurate reconstruction. We employ weight sharing in the initial stage to enhance feature quality while maintaining a compact model. Experimental results on real-world and synthetic datasets demonstrate that our approach surpasses state-of-the-art methods in both inference time and reconstruction quality.

Full-range displacement sensor with dual FBGs combined cantilever beam based on magnetic grating

We propose a full-range displacement sensor system based on double fiber Bragg gratings (FBGs) with a cantilever beam. This sensor adopts a magnetic scale as a unique transmission mechanism. By combining a simple and low-cost cantilever beam structure makes the sensing probe very easy to realize. The optimal magnetic gap was explored through multiple sets of numerical simulations. The experiment proved the sine relationship between the center wavelength shifts of FBGs and the linear displacement, proving the feasibility of this method. Results indicate that the amplitude of the tensile compression load of FBGs are 169.76 με and 296.12 με. The fitting linearity based on sinusoidal function at an air gap of 3.5 mm is 0.9663 and 0.9566. The direction of motion can be determined by the phase difference between two FBGs. Moreover, the difference of double FBGs can realize the effect of temperature compensation. Thus, this sensor can achieve non-contact, temperature-independent, and full-range measurements. It can also be exploited to measure other parameters such as angular velocity, acceleration, and magnetic field strength.

Bearing fault diagnosis method for unbalance data based on Gramian angular field

In the application of deep learning-based fault diagnosis, more often than not, the network model could perform better with a balanced dataset input, whereby the number of fault data is equivalent to that of normal data. However, in the context of real-world applications, the number of fault data is generally insufficient compared to the normal data. In this study, a new approach for fault diagnosis in unbalanced data sets is proposed using the Gramian angular field (GAF) method. Firstly, the GAF method is employed to convert one-dimensional data into two-dimensional data, which enhances the feature extraction process. Secondly, to balance the sample distribution, fault data is generated using Generative Adversarial Networks (GANs). Finally, the Residual neural network (ResNet) with an attention mechanism is utilized to improve the accuracy of fault diagnosis. The proposed method is experimentally validated using open-source bearing datasets that are published by Case Western Reserve University and the University of Ottawa. The experimental results show that the proposed method has greatly improved fault diagnosis performance in cases of data distribution imbalance, surpassing that of the compared methods.

Non-intrusive identification of building loads using EDCA-ShuffleNetV2 with fused feature visualization

Abstract Non-intrusive load monitoring (NILM) enables monitoring of appliance operation status and energy consumption without additional meters, providing innovative data support for energy management. However, current NILM systems still face challenges such as insufficient load feature information and low load identification accuracy. To utilize the load feature information and improve the accuracy of load identification more effectively, this paper first selects loads with high energy consumption and high usage frequency as the research object, makes full use of the load feature information through feature fusion, and converts the fused typical load data into an image through the Gramian summation angular field, and further carries out the identification of loads that still have similarity after feature fusion. Secondly, this paper improves the ShuffleNetV2 model structure by embedding an efficient dual-channel attention (EDCA) module in the basic feature extraction module of ShuffleNetV2 to enhance it to extract adequate load feature information. A residual structure is also introduced to mitigate the information loss and gradient disappearance issues of the model. Finally, simulations are carried out in iAWE and AMPDS datasets, and the load identification accuracy of this method reaches 99.35%, while the model parameters and floating-point operations (FLOPs) are only 3.28 M and 1.5 G, respectively. In addition, the improved EDCA-ShuffleNetV2 model has obvious advantages in terms of comprehensive model performance compared with other models.

Gramian angular field-based state-of-health estimation of lithium-ion batteries using two-dimensional convolutional neural network and bidirectional long short-term memory

Accurate assessment of the state of health (SOH) of Lithium-ion batteries (LIBs) is crucial for ensuring the stability and reliability of associated equipment. However, in practical applications, current feature extraction techniques face challenges due to process complexity and the difficulty of models in capturing the dynamic evolution of time series data. This study introduces an advanced method for predicting LIBs SOH by integrating two-dimensional (2D) convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) with the Gramian angular field (GAF) technique. Health indicators (HIs) are derived from the incremental capacity and charging voltage curves, which are transformed into two-dimensional images using GAF. Bayesian optimization determines the optimal hyperparameters for the model, which uses image data of two health factors as inputs. The results demonstrate that the proposed dual-channel(CH2) image data input model, combined with voltage data during the charging phase, achieves superior performance, with lower errors and higher accuracy, evidenced by an average root mean square error (RMSE) of 0.0112 and an average mean absolute error (MAE) of 0.0087. The model's generalization capability and comparative analysis with existing methodologies affirm its practical significance.

EEG Emotion Recognition Based on GADF and AMB‐CNN Model

ABSTRACTDeep 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.

Wind Turbine Gearbox Anomaly Detection Using Signal-to-Image Processing Algorithms and Convolutional Autoencoder

Abstract In this study, signal-to-image conversion techniques coupled with a convolutional auto encoder (CAE) are used for the detection of anomalies in the wind turbine (WT) gearbox system. Firstly, the time series data is converted to images using six different algorithms. Thereafter, these images are stacked into the multi-dimensional structures known as “data cubes”, which is finally fed into CAE for the anomaly identification. The results of this study demonstrate the enhanced efficacy of the method specially in the Gramian Angular Field model in detecting anomalies accurately, suggesting a viable path towards the implementation of dependable and affordable WT monitoring systems. This will open the door for the renewable energy industry’s condition monitoring procedures to become more automated and digitalized.

A rolling bearing fault diagnosis method for imbalanced data based on multi-scale self-attention mechanism and novel loss function

Deep learning methods are widely used in the field of rolling bearing fault diagnosis and produce good results when faced with datasets with roughly equal numbers of normal and faulty samples. However, real-world data often has a serious imbalance, with the number of fault samples being significantly less than the number of normal samples. This dataset imbalance challenges the performance of traditional deep learning methods. To address this problem, this paper proposes an efficient imbalanced data rolling bearing fault diagnosis method. The method consists of two parts: a deep learning network based on a multi-scale self-attention mechanism and a novel loss function. In terms of the deep learning network, firstly, the one-dimensional vibration signal is converted into a two-dimensional image through the Gramian angular field. This conversion maximises the inherent feature extraction capability of the network. Subsequently, the multi-scale learning capability of the network is enhanced by implementing different expansion rates for the head of the multi-scale self-attention mechanism. This nuanced approach allows the network to capture the underlying information more efficiently. Finally, the inclusion of Ghost bottlenecks and feature pyramid networks (FPNs) helps to optimise network efficiency and improve generalisation performance. A novel loss function is also proposed to make the method more suitable for imbalanced data. During the training process, the classification of samples in each class is analysed using the recall metric of imbalanced classification and the real-time recall is used as a weight to weaken the dominance of the majority class. This weighting ensures the adaptability of the method to imbalanced datasets. The proposed method is evaluated using rolling bearing datasets from Case Western Reserve University, USA, and Southeast University, China. Comparison results with other state-of-the-art deep learning methods show that the proposed method has a robust performance when dealing with imbalanced data.

Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer

Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer

CIR-DFENet: Incorporating cross-modal image representation and dual-stream feature enhanced network for activity recognition

Human activity recognition (HAR) based on wearable sensors has been widely used in various fields such as health monitoring, healthcare, and fitness due to its portability, accuracy, and real-time capabilities. Currently, advanced technologies involve converting time-series into images and deep learning for recognition, which addresses issues of subjectivity and dependence on data quality inherent in traditional processing methods. However, each of the current methods for converting time-series data into images only focuses on one type of feature representation such that one of these methods is insufficient to fully characterize the data, which results in lower accuracy in activity recognition. To address the above problems, we propose a novel method for cross-modal image representation of time-series and a dual-stream feature enhanced network model, which enables HAR based on a single-node wearable sensor. Firstly, three methods including Markov Transition Field (MTF), Recurrence Plot (RP), and Gramian Angular Field (GAF), are employed to encode the time-series into three channels (R, G, B) of color images, facilitating the fusion of features such as amplitude variation, nonlinearity, and local temporal relationship. Then, a multi-channel CNN with a Global Attention Mechanism (GAM) is employed in the model to process images, which captures both channel and spatial information. A network combining CNN and Long Short-Term Memory (LSTM) with Self-Attention Mechanism (SA) is utilized to process time-series, which captures temporal features. Simultaneously, the introduction of residual structures makes the network easy to train, thereby enhancing overall performance. The experimental results demonstrate that the proposed model can accurately identify six different gymnastic activities, achieving an accuracy rate of 99.40%. This study provides a new direction for processing time-series and offers better applications in the field of HAR based on wearable sensors.

Identification of apple watercore based on ConvNeXt and Vis/NIR spectra

This paper proposes a method for discriminating between normal apples and watercore apples using visible/near-infrared spectroscopy technology, which combines Gramian Angular Fields (GAF) encoding technology and the ConvNeXt deep learning network. The existing feature extraction methods for visible/near-infrared spectroscopy data do not perform deeper information mining on the extracted features, which results in the quality of the established model being entirely determined by the extracted features. Additionally, the process of building a visible/near-infrared spectroscopy data classification model is complex and time-consuming, and the accuracy of the established model is not high. To address these issues, the experimental visible/near-infrared spectroscopy data of apples was first transformed into two-dimensional images using Gramian Angular Summation Fields (GASF) and Gramian Angular Difference Fields (GADF) with sizes of 64, 128, 256, and 512. These images were then input into the ConvNeXt network, and the performance of different encoding methods and sizes was compared. The results showed that, under the conditions provided in this paper, the GADF encoding method with a size of 256 achieved the highest classification accuracy of 98.48%. Next, ResNet, EfficientNet, and RegNet deep learning networks were selected to classify the encoded images under the same conditions. The results above indicate that the apple variety discrimination method based on GAF encoding technology and ConvNeXt network combined with visible/near-infrared spectroscopy technology can achieve deep information mining of visible/near-infrared spectroscopy data and provide a relatively simple method for establishing qualitative classification models of visible/near-infrared spectroscopy. This method has a relatively excellent discrimination effect between normal apples and watercore apples.

A Gramian Angular Field for Constructing Graph-Based GNNs and Its Applications in Rolling Bearing Defect Detection

A Gramian Angular Field for Constructing Graph-Based GNNs and Its Applications in Rolling Bearing Defect Detection

Anomaly detection in sensor data via encoding time series into images

Detecting anomalies in multivariate time series data is crucial for maintaining the optimal functionality of control system equipment. While existing research has made significant strides in this area, the increasing complexity of industrial environments poses challenges in accurately capturing the interactions between variables. Therefore, this paper introduces an innovative anomaly detection approach that extends one-dimensional time series into two-dimensions to capture the spatial correlations within the data. Unlike traditional approaches, we utilize the Gramian Angular Field to encode the correlations between different sensors at specific time points into images, enabling precise learning of spatial information across multiple variables. Subsequently, we construct an adversarial generative model to accurately identify anomalies at the pixel level, facilitating precise localization of abnormal points. We evaluate our method using five open-source datasets from various fields. Our method outperforms state-of-the-art anomaly detection techniques across all datasets, showcasing its superior performance. Particularly, our method achieves a 11.5% increase in F1 score on the high-dimensional WADI dataset compared to the baseline method. Additionally, we conduct thorough effectiveness analysis, parameter impact experiments, significant statistical analysis, and burden analysis, confirming the efficacy of our approach in capturing both the temporal dynamics and spatial relationships inherent in multivariate time series data.

Rapid and accurate bacteria identification through deep-learning-based two-dimensional Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) offers a distinctive vibrational fingerprint of the molecules and has led to widespread applications in medical diagnosis, biochemistry, and virology. With the rapid development of artificial intelligence (AI) technology, AI-enabled Raman spectroscopic techniques, as a promising avenue for biosensing applications, have significantly boosted bacteria identification. By converting spectra into images, the dataset is enriched with more detailed information, allowing AI to identify bacterial isolates with enhanced precision. However, previous studies usually suffer from a trade-off between high-resolution spectrograms for high-accuracy identification and short training time for data processing. Here, we present an efficient bacteria identification strategy that combines deep learning models with a spectrogram encoding algorithm based on wavelet packet transform and Gramian angular field techniques. In contrast to the direct analysis of raw Raman spectra, our approach utilizes wavelet packet transform techniques to compress the spectra by a factor of 1/15, while concurrently maintaining state-of-the-art accuracy by amplifying the subtle differences via Gramian angular field techniques. The results demonstrate that our approach can achieve a 99.64 % and a 90.55 % identification accuracy for two types of bacterial isolates and thirty types of bacterial isolates, respectively, while a 90 % reduction in training time compared to the conventional methods. To verify the model's stability, Gaussian noises were superimposed on the testing dataset, showing a specific generalization ability and superior performance. This algorithm has the potential for integration into on-site testing protocols and is readily updatable with new bacterial isolates. This study provides profound insights and contributes to the current understanding of spectroscopy, paving the way for accurate and rapid bacteria identification in diverse applications of environment monitoring, food safety, microbiology, and public health.

QPOs from charged particles around magnetized black holes in braneworlds

Quasiperiodic oscillations (QPOs) are a powerful tool for testing gravity theories, probing gravitational and electromagnetic field properties, and obtaining constraints on the black hole and field parameters. This work considers charged particle dynamics near uniformly magnetized black holes in braneworlds. First, we obtain the solution of the Maxwell equation for magnetic fields and calculate the radial and angular magnetic field components. We derive and analyze the effective potential of charged particles for circular orbits and investigate the energy and angular momentum for the circular orbits. We also analyze the combined effects of magnetic interaction and braneworlds on the charged particles’ innermost stable circular orbits (ISCOs). We calculate the angular momentum of charged particles in Keplerian orbits in the presence of an external magnetic field and braneworlds. Also, we investigate frequencies of the particle oscillations along vertical and angular directions. We applied our studies on particle oscillations to the QPO studies in the relativistic precession model. Finally, we obtain constraints on magnetic interaction and braneworld parameters together with the black hole mass and QPO orbits using Monte Carlo Markov Chain (MCMC) simulation in the four-dimensional parameter space for the QPOs observed in the microquasars XTE J1550-564, GRO J1655-40 & GRS 1915-105, and at the center of galaxies M82 and Milky Way.

Research on Ploughing to Shearing Transition in Micro Milling of Titanium Alloy Using Gramian Angular Field and Machine Learning Methods

Research on Ploughing to Shearing Transition in Micro Milling of Titanium Alloy Using Gramian Angular Field and Machine Learning Methods

Acoustic emission-based leakage detection for gas safety valves: Leveraging a multi-domain encoding learning algorithm

Safety valves in gas distribution systems are crucial for preventing excessive pressure buildups. Leakage in these valves compromises system stability, causing equipment damage and environmental pollution. Acoustic emission signal analysis from the valve body is essential for detecting leakage, but these signals are often weak and easily disrupted by noise, complicating detection. This paper proposes a multi-domain encoding learning algorithm to address these challenges. Unlike conventional single-domain methods, it combines two encoding images, the Gramian angular difference field and the Markov transition field, to enhance the comprehensive expression of leakage features. A lightweight convolutional neural network reduces computing resource dependency, and a convolutional block attention mechanism improves feature identification. Experimental results demonstrate that our method detects gas leakage with an accuracy of at least 96.77%. It maintains superior performance even under intense noise interference, offering a promising solution for detecting weak gas leakages in safety valves amidst high background noise.

RS-Net: Hyperspectral Image Land Cover Classification Based on Spectral Imager Combined with Random Forest Algorithm

Recursive neural networks and transformers have recently become dominant in hyperspectral (HS) image classification due to their ability to capture long-range dependencies in spectral sequences. Despite the success of these sequential architectures, mainstream deep learning methods primarily handle two-dimensional structured data. However, challenges such as the curse of dimensionality, spectral variability, and confounding factors in hyperspectral remote sensing images limit their effectiveness, especially in remote sensing applications. To address this issue, this paper proposes a novel land cover classification algorithm that integrates random forests with a spectral transformer network structure (RS-Net). Firstly, this paper presents a combination of the Gramian Angular Field (GASF) and Gramian Angular Difference Field (GADF) algorithms, which effectively maps the multidimensional time series constructed for each pixel onto two-dimensional image features, enabling precise extraction and recognition in the backend network algorithms and improving the classification accuracy of land cover types. Secondly, to capture the relationships between features at different scales, this paper proposes a SpectralFormer network architecture using the Context and Structure Encoding (CASE) module to effectively learn dependencies between channels. This architecture enhances important features and suppresses unimportant ones, thereby addressing the semantic gap and improving the recognition capability of land cover features. Finally, the final prediction results are determined by a voting mechanism from the Random Forest algorithm, which synthesizes predictions from multiple decision trees to enhance classification stability and accuracy. To better compare the performance of RS-Net, this paper conducted extensive experiments on three benchmark HS datasets obtained from satellite and airborne imagers, comparing various classic neural network models. Surprisingly, the RS-Net algorithm achieves high performance and efficiency, offering a new and effective tool for land cover classification.