Markov Transition Field Research Articles

Forecasting Bitcoin volatility using machine learning techniques

This paper studies the Bitcoin volatility forecasting performance between popular traditional econometric models and machine learning techniques. We compare the 1-day to 2-month ahead forecasting performance of the Long Short-Term Memory (LSTM) and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM) model to the traditional models. We find that neural networks outperform Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models for all forecasting horizons. Furthermore, the LSTM model outperforms the Heterogeneous Autoregressive (HAR) model and by integrating the Markov Transition Field (MTF) into the CNN-LSTM model, we achieve superior forecasting results in the short-term, particularly for the 7-day forecasts.

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.

Quantitative Detection of Refrigerant Charge Faults in Multi-unit Air Conditioning Systems Based on Machine Learning Algorithms: Détection quantitative des défauts de charge de réfrigérant dans les systèmes de climatisation multi-unités basée sur des algorithmes d'apprentissage automatique

Refrigerant charging discrepancies constitute the predominant malfunctions in air conditioning systems. Achieving the optimal charging level is crucial for system performance, underscoring the importance of precise refrigerant level prediction. This study introduces an algorithm designed for the quantitative detection of refrigerant charging errors by integrating the Markov Transition Field (MTF), Convolutional Neural Networks (CNN), and Multi-head Self-Attention (MSA) mechanisms. A high-precision enthalpy difference chamber was employed to establish a Variable Refrigerant Flow (VRF) refrigerant charging test bench. This setup facilitated the analysis of system parameter sensitivity to charging faults and aided in the creation of a training dataset for the algorithm. Comparative analysis was conducted against Support Vector Machines (SVM), Random Forests (RF), CNN with Self-Attention (AT), and MTF-CNN-MSA. The findings reveal that our method adeptly captures temporal dependencies and dynamic shifts in time series as visual representations, offering novel insights for discerning fault patterns within such data. Notably, the maximum pressure variations at high-pressure and low-pressure points were 0.25 MPa and 0.07 MPa, respectively, with temperature shifts of 12°C and 3.5°C at the high and low-temperature points. The high-pressure and high-temperature points are particularly sensitive to changes in refrigerant charging, and parameters from these sections were utilized to construct the dataset. The CNN-MSA algorithm demonstrates consistent performance across various fault types, effectively delineating fault characteristics. The accuracies achieved by SVM, RF, CNN-AT, and MTF-CNN-MSA were 84.38%, 73.75%, 88.13%, and 93.75%, respectively. In comparison, the CNN-MSA algorithm was able to more accurately detect refrigerant charge faults at different levels.

Classification and identification of extreme wind events by CNNs based on Shapelets and improved GASF-GADF

In this manuscript, we propose an automatic classification and recognition method for extreme wind events based on Convolutional Neural Networks (CNNs) and combining the Shapelet Transform (ST) algorithm with the improved Gramian Angular Summation Field - Gramian Angular Difference Field (GASF-GADF) 2D images construction format. First, a CNN model suitable for wind speed time series 2D images classification and recognition among five mainstream CNNs (ResNet-50, ShuffleNet0.5 × , DenseNet-121, EfficientNet-B2, and EfficientNetV2-S) is preferred based on the basic Gramian Angular Field (GAF) method; then, the improved GASF-GADF images construction format is proposed, and the optimal CNN is used to compare the classification and recognition results based on other three image conversion methods: Markov Transition Field (MTF), GASF, GADF. Last, it is proposed to utilize the ST algorithm to extract the feature subsequence Shapelets of wind speed time series to further improve the classification and recognition effect on extreme wind events. The effectiveness and applicability of the proposed method were verified through three extreme wind event datasets in this paper.The results show that the combination of Shapelets and the improved GASF-GADF images transformation method proposed in this paper can effectively enhance the classification and recognition of extreme wind events by CNNs. Among them, EfficientNetV2-S combined with the method proposed in this paper achieves 99.50%, 99.50% and 97.50% recognition Accuracy for thunderstorm, gust front and typhoon, respectively. Meanwhile, it still has good robustness for extreme wind events disturbed by noise.

A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem

With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD ) and conditional domain adversarial network with entropy conditioning (CDAN+E ) is employed to align the source and target domains, thereby enhancing the model’s cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model.

Interpretable coal-rock cutting vibration recognition with Markov transition field and selective neural networks

Interpretable coal-rock cutting vibration recognition with Markov transition field and selective neural networks

A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

Abstract Recently, deep learning has received widespread attention in the field of bearing fault diagnosis due to its powerful feature learning capability. However, when the actual working conditions are complex and variable, the fault information in a single domain is limited, making it difficult to achieve high accuracy. To overcome these challenges, this paper proposes a bearing fault diagnosis method based on the Markov transition field, continuous wavelet transform (CWT), and dual-channel convolutional neural network (CNN). The method combines the descriptive ability of the Markov model for state transfer, the time-frequency analysis ability of CWT for signal, and the excellent performance of CNN with attention mechanism in feature extraction and classification. Specifically, we first propose a multi-channel Markov transition field method, which is combined with CWT to obtain two different representations of two-dimensional (2D) images. To comprehensively mine fault information, we further propose a dual-channel CNN with an attention mechanism. The design of this network structure aims to extract multi-level features from two types of 2D images. At the same time, we designed and embedded an attention mechanism to enable the network to focus more on extracting effective features, thereby improving the performance and accuracy of the network. To verify the effectiveness of the proposed method, three datasets were used for empirical research. The results show that this method exhibits superior performance in bearing fault diagnosis and has higher accuracy compared to traditional methods.

Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

Emotion classification using electrocardiogram and machine learning: A study on the effect of windowing techniques

Automated emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in human–computer interaction, healthcare, and psychology. Electrocardiogram (ECG) signals are widely used due to their non-invasiveness, high temporal resolution, and direct relationship with the autonomic nervous system. In this study, we propose a novel approach for ECG-based emotion recognition using time-series to image encoding techniques and texture-based features combined with machine learning algorithms and deep learning architectures. The ECG data used in this study were obtained from the Continuously Annotated Signals of Emotion (CASE) and Wearable Stress and Affect Detection (WESAD) datasets. We categorized emotional states based on valence and arousal annotations into four classes: High-Valence High-Arousal, High-Valence Low-Arousal, Low-Valence High-Arousal, and Low-Valence Low-Arousal. The ECGs were segmented into 5 and 7-window segments and transformed into 2D representations using Gramian Angular Summation Field, Markov Transition Field, Recurrence Plot (RP), and the triple-channel fusion of all these images. Total of 85 textural features based on the Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Run Length Matrix, Zernike’s Moments, Hu’s Moments, Fractal Dimension Texture Analysis (FDTA), and First-Order Statistics were extracted. Classifiers, namely Random Forest, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), 1D Convolutional Neural Network (CNN), and Multi-head Attention Network were considered to classify the emotional states. The performance of the classifiers varied depending on the time-series to image encoding technique, segmentation approaches, and the classifier employed. We achieved the highest Weighted F-measure (F-m) of 94.91% (RP + XGB) and 86.78% (RP + SVM) using the 7-window and 5-window approaches, respectively. Our proposed 1D CNN architecture achieved the highest classification metrics (F-m = 92.52%, Balanced accuracy = 92.0%, Recall = 91.96%, and Precision = 93.16%) with RP images in a 7-window approach. GLCM and FDTA features made significant contributions to the classification of emotional states. Overall, our results suggest that the proposed method holds promise for developing more accurate and efficient emotion recognition systems.

A short time series rolling bearing fault diagnosis method based on FMTF-CNN

The signal characteristics are extracted directly from the convolutional level when the Convolutional Neural Network (CNN) is used as a fault diagnosis method in most instances. Some scholars have proposed to use the Markov Transition Field (MTF) to extract the signal characteristics before convolution. However, for time-domain signals, the fault characteristics extraction is difficult, and the training process of the neural network parameters is slow. Therefore, in this paper, based on Fast Fourier Transform (FFT) and MTF algorithm, the Fourier Markov Transition Field (FMTF) characteristics extraction method in frequency domain is proposed, and a large number of experiments are carried on the public bearing data set of Jiangnan University. The final verification shows that the FMTF-CNN method has higher accuracy, faster training process and more accurate characteristics extraction than the traditional MTF-CNN diagnosis method.

Retired Battery Screening Based on Markov Transition Field and Swin Transformer

Retired Battery Screening Based on Markov Transition Field and Swin Transformer

Intelligent fault diagnosis of machinery based on hybrid deep learning with multi temporal correlation feature fusion

AbstractWith the advent of intelligent manufacturing era, higher requirements are put forward for the fault diagnosis technology of machinery. The existing data‐driven approaches either rely on specialized empirical knowledge for feature analysis, or adopt single deep neural network topology structure for automatic feature extraction with compromise of certain information loss especially the time‐series information's sacrifice, which both eventually affect the diagnosis accuracy. To address the issue, this paper proposes a novel multi‐temporal correlation feature fusion net (MTCFF‐Net) for intelligent fault diagnosis, which can capture and retain time‐series fault feature information from different dimensions. MTCFF‐Net contains four sub‐networks, which are long and short‐term memory (LSTM) sub‐network, Gramian angular summation field (GASF)‐GhostNet sub‐network and Markov transition field (MTF)‐GhostNet sub‐network and feature fusion sub‐network. Features of different dimensional are extracted through parallel LSTM sub‐network, GASF‐GhostNet sub‐network and MTF‐GhostNet sub‐network, and then fused by feature fusion sub‐network for accurate fault diagnosis. Two fault diagnosis experimental studies on bearings are implemented to validate the effectiveness and generalization of the proposed MTCFF‐Net. Experimental results demonstrate that the proposed model is superior to other comparative approaches.

Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19

The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks.

Predicting low-cycle fatigue-induced fracture in reinforcing bars: A CNN-based approach

In low-damage structures such as rocking columns in bridges where the column ends are protected from spalling through confinement, the longitudinal reinforcing bars undergo reversing strain cycles, leading to fracture due to low-cycle fatigue. Several challenges arise due to the unsuitability of strain gauges to measure high strain ranges, in addition to the inability to visually inspect reinforcing bars after seismic events due to the use of confining details at the ends. Building on previous research that identified the fracture of reinforcing bars using column ends’ rotation to estimate reinforcing bars’ strains in conjunction with low-cycle fatigue models, this paper presents substantial development to predict the low-cycle fatigue-induced fracture of reinforcing bars using convolutional neural networks (CNNs) solely from strain time series data. The fracture was identified through strain measurements measured from a shake table testing of a quarter-scale, two-span resilient bridge specimen subjected to seismic excitations at the Earthquake Laboratory at the University of Nevada, Reno. The developed CNN model utilizes the Markov Transition Field technique to encode the strain time series into images and then uses the encoded images as input for channels in the input layer. These images are stacked in sequence to create a 3D array with 11 channels, one for each of the 11 different earthquake excitations that caused the damage. A three-layered CNN architecture with Adam optimizer was employed in training the model, achieving an accuracy of 100 % during training and more than 96 % during testing. To evaluate the model's performance, three distinct training/testing scenarios are proposed. The results demonstrate the efficacy of using CNNs to detect and characterize damage in structural elements using strain data. This approach has the potential to revolutionize the estimation of material fracture due to low-cycle fatigue in scientific fields using only the recoded strains.

A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals

Data information security on wearable devices has emerged as a significant concern among users, so it becomes urgent to explore authentication methods based on wearable devices. Using PhotoPlethysmoGraphy (PPG) signals for identity authentication has been proven effective in biometric authentication. This paper proposes a convolutional neural network authentication method based on 2D PPG signals applied to wearable devices. This method uses Markov Transition Field technology to convert one-dimensional PPG signal data into two-dimensional image data, which not only retains the characteristics of the signal but also enriches the spatial information. Afterward, considering that wearable devices usually have limited resources, a lightweight convolutional neural network model is also designed in this method, which reduces resource consumption and computational complexity while ensuring high performance. It is proved experimentally that this method achieves 98.62% and 96.17% accuracy on the training set and test set, respectively, an undeniable advantage compared to the traditional one-dimensional deep learning method and the classical two-dimensional deep learning method.

Research on fault diagnosis method based on the Markov transition field with enhanced properties and AM-MSCNN under different external environmental interference

The mechanical equipment often faces complex working environments in practical operating conditions, and the external environmental interference generated by operating conditions, environmental factors and other components causes the vibration signals to exhibit characteristics with frequency distortion and multi-modality. The existing fault diagnosis methods rarely consider the issue of external environmental interference. Aiming at the background of fault diagnosis under external environment interference, a fault diagnosis method based on Markov transfer field (MTF) with enhanced properties and multi-scale convolutional neural network with attention mechanism (AM-MSCNN) is proposed. The fault features embedded in vibration signals under external environmental interference can be extracted, and an important contribution to the fault diagnosis method under external environmental interference can be made. Firstly, an interference mode selection model based on symplectic geometry modal decomposition is constructed to address the issues of distortion and multi-modality caused by external environmental interference. Next, a two-dimensional feature extraction method based on the MTF with enhanced properties is established. The challenge of extracting temporal correlation features from one-dimensional vibration signals affected by external environmental interference is addressed by Markov transition probability. The impact of external environmental interference can be mitigated, and that has strong anti-interference capability and robustness. Finally, an attention mechanism that can adaptively assign weights is designed, and the AM-MSCNN model is designed to effectively extract global features by incorporating attention mechanisms in the parallel layers of MSCNN and the attention mechanism helps to suppress external environmental interference and improve the diagnostic results. An experimental platform for simulating the typical faults under external environmental interference is constructed, and the experimental results demonstrate that the proposed method exhibits superior generalization performance under varying degrees of different interference environments. The overall average accuracy reaches 92.2%, and the highest accuracy reaches 94.0% for external interference working conditions.

An intelligent complex power quality disturbance recognition method based on two dimension encoding conversion and machine vison

This study proposes an intelligent methodology for accurate identification of complex power quality disturbance (PQD) based on 2D encoding conversion and machine vision. Firstly, a 2D image encoding method as improved Markov transition field (IMTF) is proposed to convert 1D PQD signals to 2D images, which can realize the enhancement of disturbance feature display. It can solve the information loss and duplication problems of traditional Markov transition field (MTF). Then, a new classification method AFF-ResNetXt50 is proposed to realize the PQD accurate recognition. It can address the problem that traditional ResNetXt50 is susceptible to the interference from irrelevant information and thus severely degrades the classification accuracy. Compared with other popular methods, this proposed method can obtain higher accuracy as 98.5 % for PQD signals recognition. Finally, PQD experimental platform is established, and IEEE PES dataset is also used to certify the proposed method has the excellent performances and robustness.

Intelligent fault diagnosis of railway pantograph using a novel graph construction methodology

Railway pantographs provide power for railway vehicles by conducting electrical energy from overhead catenary. The failure of the pantograph tends to damage the contact quality between the pantograph and the catenary, reducing the transmission efficiency of electric energy. Hence, fault diagnosis of pantograph plays a significant role in expanding the service life of railway vehicles. In this work, a novel graph construction method is proposed for the fault diagnosis of pantographs combined with a graph neural network (GNN). In the graph construction method, 1D load signals collected from the test pantograph are firstly transformed into multiple 2D images with the same size in both time and frequency domains using Gramian angular field, Markov transition field and recurrence plot. Secondly, pixel values in images are regarded as features in vertexes of graphs, and graphs can be constructed by connecting neighbor vertexes. Finally, the GNN model is trained by constructed graphs for obtaining the fault diagnosis model of pantographs. Laboratory experiments are implemented to show the advantages of the proposed method by comparing it with other conventional methods.

Research on non-intrusive load identification method based on multi-feature fusion with improved shufflenetv2

Non-intrusive load monitoring is a significant advancement in energy conservation and smart electricity usage as it enables the identification of load status and equipment type without the need for extensive sensing instruments. As one of the important steps of non-intrusive load monitoring, non-intrusive load identification plays a crucial role in the correct identification of electrical appliances. In this paper, a novel approach is presented to amalgamate multiple appliance features into a new identification feature, which is then used for appliance recognition through an improved lightweight deep learning model. This addresses the issue of low accuracy when using a single feature for appliance identification. The core concept involves initially capturing the current data of an appliance during steady state operation and fusing the image features of the appliance encoded by Gramian summation angular field, Gramian difference angular field, and Markov transition field into new recognition features for each appliance using average weighting. Subsequently, a shufflenetv2 lightweight deep learning model based on the squeeze -and-excitation module is used to mine the constructed load feature information for the load classification task. The method is then experimentally validated on the Self-test Dataset, PLAID, and WHITED dataset, resulting in recognition accuracies of 100%, 98.214%, and 99.745%, respectively. These results demonstrate the effectiveness of the proposed method in significantly improving recognition performance compared to the original approach.

Identifying pediatric heart murmurs and distinguishing innocent from pathologic using deep learning

ObjectiveTo develop a deep learning algorithm to perform multi-class classification of normal pediatric heart sounds, innocent murmurs, and pathologic murmurs. MethodsWe prospectively enrolled children under age 18 being evaluated by the Division of Pediatric Cardiology. Parents provided consent for a deidentified recording of their child's heart sounds with a digital stethoscope. Innocent murmurs were validated by a pediatric cardiologist and pathologic murmurs were validated by echocardiogram. To augment our collection of normal heart sounds, we utilized a public database of pediatric heart sound recordings (Oliveira, 2022). We propose two novel approaches for this audio classification task. We train a vision transformer on either Markov transition field or Gramian angular field image representations of the frequency spectrum. We benchmark our results against a ResNet-50 CNN trained on spectrogram images. ResultsOur final dataset consisted of 366 normal heart sounds, 175 innocent murmurs, and 216 pathologic murmurs. Innocent murmurs collected include Still's murmur, venous hum, and flow murmurs. Pathologic murmurs included ventricular septal defect, tetralogy of Fallot, aortic regurgitation, aortic stenosis, pulmonary stenosis, mitral regurgitation and stenosis, and tricuspid regurgitation. We find that the Vision Transformer consistently outperforms the ResNet-50 on all three image representations, and that the Gramian angular field is the superior image representation for pediatric heart sounds. We calculated a one-vs-rest multi-class ROC curve for each of the three classes. Our best model achieves an area under the curve (AUC) value of 0.92 ± 0.05, 0.83 ± 0.04, and 0.88 ± 0.04 for identifying normal heart sounds, innocent murmurs, and pathologic murmurs, respectively. ConclusionWe present two novel methods for pediatric heart sound classification, which outperforms the current standard of using a convolutional neural network trained on spectrogram images. To our knowledge, we are the first to demonstrate multi-class classification of pediatric murmurs. Multiclass output affords a more explainable and interpretable model, which can facilitate further model improvement in the downstream model development cycle and enhance clinician trust and therefore adoption.