One-dimensional Time Research Articles

Gearbox fault diagnosis method based on improved semi-supervised MTDL and GAF

Aiming at the problem that it takes a long time and high cost to obtain complete labeled data under intelligent fault diagnosis and unlabeled data is not used. This paper proposes an improved semi-supervised mean teacher deep learning (MTDL) and Gramian angle field (GAF) fusion diagnostic method. This method fully utilizes a small number of labeled samples and a large number of unlabeled samples to deeply mine invisible fault features and potential physical correlations. At the same time, it solves the problem of losing the inter-data correlation structure when one-dimensional time series signals are used as inputs for neural networks. The GAF-MTDL method uses consistency regularization and modifies the network structure in the mean teacher algorithm into a semi-supervised deep learning model enhanced by WideResNet. The experimental results show that the proposed GAF-MTDL method saves a lot of manual labeling costs, improves the recognition accuracy and generalization ability, and can achieve excellent prediction accuracy with very little labeled data. In the end, the accuracy of planetary gear fault identification reached 98.22% under the labeling rate of 20%, and the accuracy of fault identification reached 99.98% through the verification of the bearing data set of Case Western Reserve University. The value of this research is to bring an efficient and low-cost technology to the field of industrial intelligent fault diagnosis, which can significantly improve the accuracy of fault identification.

Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening

Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as “fingerprint”, in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.

Анализ и прогнозирование трафика современных телекоммуникационных систем на основе методов искусственного интеллекта

In recent years, artificial intelligence technologies have demonstrated significant success in solving the problem of traffic analysis and forecasting in various telecommunication systems. Forecasting allows the telecom operator to know about the future behavior of the network, take timely necessary measures to improve the quality of customer service, and decide on the need to install or upgrade equipment. Using data collected from IoT mobile devices as an example, this article provides an overview and analysis of various time series forecasting models describing the traffic behavior of telecommunication systems. Forecasting models such as the exponential smoothing method, linear regression, the autoregressive integrated moving average (ARIMA) method, the support vector machine regression method, the N-BEATS method, which uses fully connected layers of a neural network for forecasting a one-dimensional time series, are discussed; the features of some of them are briefly outlined. For a specific data array, data preparation operations are described: removing unused columns, replacing missing data on transaction durations with their median values, and describing the main statistical characteristics of the data array. A preliminary data analysis is presented, which consists of using smoothing methods: moving average and exponential smoothing. The process of training models and a comparative analysis of the quality of their training are described. For this data set, it was concluded that for the UDP protocol the ARIMA model has the best learning quality, for the TCP protocol - linear regression and the Theta model, for the HTTPS protocol – linear regression, ARIMA and N-BEATS.

Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN)

As a commonly used mode of transportation in people’s daily lives, the normal operation of railway transportation is crucial. The track circuit, as a key component of the railway transportation system, is prone to malfunctions due to environmental factors. However, the current method of inspecting track circuit faults still relies on the experience of on-site personnel. In order to improve the efficiency and accuracy of fault diagnosis, we propose to establish an intelligent fault diagnosis system. Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). The LSTM network is established on the basis of fault data and used for ZPW-2000A track circuit fault diagnosis. However, the use of a single LSTM network has a high error rate in the common fault diagnosis of track circuits. Therefore, this paper proposes a feature extraction method based on the UNet network. This method is used to extract the features of the original data and then input them into the LSTM network for fault diagnosis. Through experiments with on-site fault data, it has been verified that this method can accurately classify seven common track circuit faults. Finally, the superiority of the method is verified by comparing it with other commonly used fault classification methods.

Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data

Fracture zones in front of tunnel faces can easily cause falling blocks and landslides during the construction process. Using seismic waves and ground-penetrating radar (GPR) data, we extracted the features of fracture zones and achieved the advanced prediction of tunnel fracture zones. The energy variation in the reflected waves propagated by seismic waves at interfaces with different impedances of contact waves was found to manifest as positive and negative reflections, and the amplitude of reflected signals within the fracture zone areas thus increased. We designed a superimposed velocity spectrum, divided the areas of variation in wave velocity, and constructed the three-dimensional spatial distribution of the tunnel fracture zones. Based on the phase change, increase in amplitude, and increase in the center-frequency characteristics of the one-dimensional time waveform of the electromagnetic waves in the fault zone area (A-scan), we located the characteristic points of the fracture zones and observed the occurrence of in-phase axis misalignment in two-dimensional scanning (B-scan). We then implemented the identification of fracture zones. This method predicted the fractured area in the rock surrounding the Liangwangshan Tunnel, and during the tunnel excavation, the fracture zones appeared in the recognition area.

Estimating the turbulent kinetic energy dissipation rate from one-dimensional velocity measurements in time

Abstract. The turbulent kinetic energy dissipation rate is one of the most important quantities characterizing turbulence. Experimental studies of a turbulent flow in terms of the energy dissipation rate often rely on one-dimensional measurements of the flow velocity fluctuations in time. In this work, we first use direct numerical simulation of stationary homogeneous isotropic turbulence at Taylor-scale Reynolds numbers 74≤Rλ≤321 to evaluate different methods for inferring the energy dissipation rate from one-dimensional velocity time records. We systematically investigate the influence of the finite turbulence intensity and the misalignment between the mean flow direction and the measurement probe, and we derive analytical expressions for the errors associated with these parameters. We further investigate how statistical averaging for different time windows affects the results as a function of Rλ. The results are then combined with Max Planck Variable Density Turbulence Tunnel hot-wire measurements at 147≤Rλ≤5864 to investigate flow conditions similar to those in the atmospheric boundary layer. Finally, practical guidelines for estimating the energy dissipation rate from one-dimensional atmospheric velocity records are given.

Bearing fault diagnosis based on the feature enhancement of improved mean differential SDP images★ ★This work is supported by National Natural Science Foundation of China [No. 61603149]; Shandong Province Natural Science Foundation [No. ZR2023MF102

In the fault diagnosis of mechanical equipment, vibrational signals often contain noise. In order to solve the problem of rolling bearing fault diagnosis under the influence of noise, an image enhancement method based on mean difference images is proposed. Convert one-dimensional time series into two-dimensional image data by using the symmetrized dot pattern method. The data are processed and grouped by variational mode decomposition to obtain a mean image with stable features. A feature enhancement method based on improved mean difference images is used to achieve data mining of fault features. The improved Canberra distance is used as the classification basis to realize the accurate classification of rolling bearing faults. Finally, the classification effect of the method is verified by experiments. The experimental results show that the image enhancement method proposed in this paper can improve fault features in the image and has a good anti-noise ability.

Ground-motion simulations using two-dimensional convolution condition adversarial neural network(2D-cGAN)

In this paper, an integrated framework based on conditional adversarial neural network (cGAN) is established to simulate ground motions for earthquake scenarios with different magnitude, distance, and local site conditions. A ground motion dataset is created for training the network, within which most data are observed accelerograms collected from PEER and gaps for certain magnitude-site conditions are filled with accelerograms simulated using stochastic finite-fault modeling. During the learning of cGAN, the dataset is classified first with multi-labels according to local site conditions, and the multi-labels are set to be the conditional information Y for the network. Effects of using one-dimensional convolution (Conv1D) and two-dimensional convolution (Conv2D) in cGAN are tested in terms of the time-domain and frequency-domain characteristics of the generated ground motion. The test proves the strengths of Conv2D in capturing high-dimensional temporal and spectral characteristics from one-dimensional time series of ground motion. Ground motions generated using 2D-cGAN network are compared to the measures predicted by widely-used ground motion prediction equations, through which the reliability of 2D-cGAN network on ground motion simulation is verified.

Identification of composite power quality disturbances based on relative position matrix

With the integration of large-scale nonlinear loads and distributed power sources into the grid, composite power quality disturbances (PQDs) events are becoming increasingly common, which significantly degrade the quality of power supply. Therefore, this paper focuses on studying the accurate classification of composite PQDs to mitigate the risk of power quality deterioration. However, traditional classification methods perform barely satisfactory in terms of accuracy and robustness in the classification of PQDs. To address these issues, this paper proposes a method for recognizing composite PQDs based on relative position matrix (RPM). Initially, utilizing the RPM method, the initial one-dimensional PQD time series data is transformed into two-dimensional image data while preserving its high-frequency characteristics. This process results in the creation of an informative and feature-rich image training set. Subsequently, an end-to-end framework for PQDs classification was developed. The framework utilizes convolutional neural networks to automatically extract multi-scale spatial and temporal features from image data. This design aims to automate the classification of composite PQDs, eliminating the need for labor-intensive manual signal processing and feature extraction. This integration ensures a more accurate and robust classification. Finally, the proposed method is tested on a case involving 30 types of PQDs at varying noise levels and compared with existing power quality disturbance classification methods, and results show that the proposed method has better performance than the previously established methods.

BiLSTM-FCN based vibration signal diagnosis of smart grid cables

Abstract Cable faults threaten the safe and stable operation of smart grids, and vibration signal diagnosis research on cables based on artificial intelligence technology can effectively enhance the reliability of smart grids. In order to improve the speed and accuracy of cable defect identification, this paper proposes a partial discharge identification method for cables based on a fully convolutional bidirectional long short-term memory neural network (BiLSTM-FCN). The time-domain characteristics of different working conditions under industrial frequency AC voltage are collected by building a vibration signal-based partial discharge test platform for cables. The Overlap strategy enhances the data set. The BiLSTM layer is introduced to process the one-dimensional time domain waveform signal, and combined with the local detail features extracted by the FCN layer, the data features are more abundant. Thus, a more accurate diagnosis of localized discharge in high voltage cables under different operating conditions. The results show that the diagnostic accuracy based on the BiLSTM-FCN model reaches 92.2%, which is 1.2% and 1.4% higher compared to the FCN model and the BiLSTM model. BiLSTM-FCN model possesses a better recognition effect and faster recognition speed in the identification of partial discharge defects type, which can effectively achieve the automatic detection of abnormal fault nodes of smart grid cables. It is significant for realizing online dynamic evaluation of small portable online monitoring equipment and provides a reference for future related research.

Research on the design of a novel composite solenoid model ship simulation magnetic source based on DTW

In order to study the simulation ability of a solenoid on a ship magnetic field, a new composite model based on a double row magnetic dipole array model with an ellipsoid was designed. The time-domain characteristics, such as magnetic field characteristics, magnetic inclination cosine, magnetic field scalar gradient, and magnetic field spatial distribution characteristics, were extracted. It was verified that the model can accurately simulate the magnetic field characteristics of ships through ship model experiments and simulation analysis. To further quantitatively and accurately analyze the simulation ability of the composite model for ship magnetic fields, a dynamic time wrapping measurement method based on one-dimensional time series was adopted to calculate the similarity of four characteristic quantities: magnetic field vector, magnetic field modulus, magnetic inclination cosine, and magnetic field scalar gradient. The results showed that the composite model has high simulation ability for ship magnetic fields.

Comparative analysis of the application of different types of neural networks to the recognition of one-dimensional signals

In the processes of determining the properties of materials and structures based on the study of the response to a given dynamic impact, the problem of analysing a one-dimensional time signal and its classification arises. One of the effective approaches to solving it is the use of artificial neural networks with generalized properties of approximation and data filtering. The paper investigates the effectiveness of using fully connected, recurrent and convolutional neural networks to problems of impact indentation to determine the strength properties of metals and elastic moduli of layered structures of non-rigid highways.

Friction Saturated Limit Cycle Oscillations—Test Rig Design and Validation of Numerical Prediction Methods

Abstract In this paper, an experimental test rig for friction saturated limit cycle oscillations is proposed to provide a validation basis for corresponding numerical methods. Having in mind the application of turbine blades, an instrumented beam-like structure equipped with an adjustable velocity feedback loop and dry frictional contacts is designed and investigated. After dimensioning the test rig by means of a simplified one-dimensional beam model and time domain simulations, the specific requirements of limit cycle oscillations for the design of the frictional contact, the velocity feedback loop and the excitation system are discussed and possible solutions are presented. Also appropriate measuring principles and evaluation techniques are assessed. After commissioning of the test rig, the influence of the negative damping and the normal contact force on the limit cycle oscillations is measured and the practical stability is investigated. The test rig shows linear dynamics for sticking contact and highly repeatable limit cycles. The measured results are discussed regarding the consistency with theory and compared to the predictions of a three dimensional reduced order model solved in frequency domain by the harmonic balance solver OrAgL. It is demonstrated that the numerical modeling strategy is able to accurately reproduce the measured limit cycle oscillations, which stabilized for different contact normal forces and self-excitation levels.

A new similarity measurement method for time series based on image fusion of recurrence plots and wavelet scalogram

The core of data series classification and clustering is how to evaluate the similarity between time series. However, for one-dimensional time series, the vast majority of similarity measurement methods currently relied on one-dimensional information. Although some classifications of time series were achieved by the images transformed from time series, they mostly relied on a single imaging mechanism (time domain or frequency domain). In order to give a new idea for time series similarity measurement, this paper proposed a similarity measurement method of time series based on the fused images. According to recurrence plots (RP) and wavelet scalogram (WS), the original time series was first transformed into images. Then, RP and WS were fused into an image. Next, the feature in the image was extracted by ResNet-18. Finally, the similarity of the time series can be obtained by the Euclidean distance between the features extracted by ResNet-18. The proposed method was verified by the time series from UCR Time Series Classification/Clustering Homepage. The results showed that the similarity measurement of time series can be effectively improved by fusing the images obtained by different imaging mechanisms in the time domain and frequency domain.

Optimizing Speech Emotion Recognition with Hilbert Curve and convolutional neural network

In the realm of speech emotion recognition, researchers strive to refine representation methods for improved emotional information capture. Traditional one-dimensional time series classification falls short in expressing intricate emotional patterns present in speech signals, posing challenges in accuracy and robustness. This study introduces an innovative algorithm leveraging Hilbert curves to transform one-dimensional speech data into two-dimensional form, enhancing feature extraction accuracy. A tiling module based on Hilbert curve maximizes Hilbert curve arrangements for improved emotional information capture. Results reveal spatial efficiency gains up to 23,195 times pixel units, enhancing data storage. With an exceptional 98.73% accuracy, the proposed approach traditional methods, affirming its superior emotion classification performance on the same dataset. These empirical findings underscore the effectiveness of our proposed method in advancing speech emotion recognition.

Novel EMD with Optimal Mode Selector, MFCC, and 2DCNN for Leak Detection and Localization in Water Pipeline

Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and cross-correlation for leak localization. The existing methods’ complexity is very high, as they detect and localize the leak using two different architectures. This paper aims to present an independent architecture with a single sensor for detecting and localizing leaks with enhanced performance. The proposed approach combines a novel EMD with an optimal mode selector, an MFCC, and a two-dimensional convolutional neural network (2DCNN). The suggested technique uses acousto-optic sensor data from a real-time water pipeline setup in UTAR, Malaysia. The collected data are noisy, redundant, and a one-dimensional time series. So, the data must be denoised and prepared before being fed to the 2DCNN for detection and localization. The proposed novel EMD with an optimal mode selector denoises the one-dimensional time series data and identifies the desired IMF. The desired IMF is passed to the MFCC and then to 2DCNN to detect and localize the leak. The assessment criteria employed in this study are prediction accuracy, precision, recall, F-score, and R-squared. The existing MFCC helps validate the proposed method’s leak detection-only credibility. This paper also implements EMD variants to show the novel EMD’s importance with the optimal mode selector algorithm. The reliability of the proposed novel EMD with an optimal mode selector, an MFCC, and a 2DCNN is cross-verified with cross-correlation. The findings demonstrate that the novel EMD with an optimal mode selector, an MFCC, and a 2DCNN surpasses the alternative leak detection-only methods and leak detection and localization methods. The proposed leak detection method gives 99.99% accuracy across all the metrics. The proposed leak detection and localization method’s prediction accuracy is 99.54%, precision is 98.92%, recall is 98.86%, F-score is 98.89%, and R-square is 99.09%.

CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis

Data-driven fault diagnosis has become a hot topic of research in recent years, due to its wide applicability, high accuracy, and ease of modeling. In data-driven fault diagnosis, feature extraction is usually performed through a combination of one-dimensional time domain or frequency domain analysis methods. However, the constraints of data format and diagnostic methods give rise to three primary issues: inadequate utilization, expression, and mining of fault features, thereby impeding the broader application of data-driven methods. This paper presents a fault diagnosis framework that integrates cross-domain fusion images and a lightweight feature-enhanced network to address these issues. We leverage the temporal and frequency features of raw time series data, transforming them into images. This approach not only offers a greater amount of fault information but also highlights the inherent features more prominently. In terms of diagnosis methods, a novel feature-enhanced network model is proposed. This model takes images generated by a cross-domain image fusion module as inputs and then goes through a customized feature extraction module, a feature fusion module, a feature enhancement module, and a classification module to achieve high-precision and fast fault diagnosis. The effectiveness of this framework is validated on two publicly available datasets. The results show that the average accuracy of this fault diagnosis framework can reach 100%, surpassing the findings of existing research. The experiments on noise resistance further showcase the superiority of the proposed framework. It is expected that the framework presented in this study will provide a new perspective and direction for fault diagnosis.

Tuning quantum pathway interference in two-color laser photoemission using DC bias

Coherent control of quantum systems depends on the manipulation of quantum interference through external fields. In this work, we investigate the effects of DC bias field on coherent control of quantum pathways in two-color laser photoemission using exact analytical solutions of the one-dimensional time dependent Schrödinger equation. Increasing DC bias lowers and narrows the surface potential barrier, shifting the dominant emission to lower order multiphoton photoemission, photo-assisted tunneling and then direct tunneling. Those lower order photon absorption processes result in fewer possible pathways, and therefore modulation of photoemission current can be suppressed as DC field increases. It is shown that a maximum modulation depth of 99.4% can be achieved for a gold emitter at local DC bias F 0 = 0.5 V nm−1, fundamental (800 nm) laser field F 1 = 2.6 V nm−1 and second harmonic laser field F 2 = 0.25 V nm−1 . For a given set of input parameters, the total photoemission consists of different k-photon processes, each of which has their own different multiple possible pathways and interference effects. However, the quantum pathways and their interference for the dominant k-photon process and for the total photoemission probability show the same trends. This study demonstrates strong flexibility in tuning two-color lasers induced photoemission using a DC bias and provides insights into coherent control schemes of general quantum systems.

Derivative and partial integral methods and Gauss integral's indefinite integral solution and its use in wave function in quantum mechanics and exact solutions of the wave function depending on position and time

This study, , solution of Gaussian Integral with differential equation [18,19]; By using the partial integral method, the indefinite integral solution by taking x under the differential [29,30,31] and the indefinite integral solution by taking the Gaussian integral under the differential [33,38,39] both harmonic series and function solutions are found. Indefinite integral solution of Gaussian Integral [38] In Quantum Physics, the wave function solution f(x,α) in terms of α variable in x position and k space by substituting it in wave function [44,45] and in one-dimensional time dependent Schrödinger equation, wave function equation [66, 67] is found. When the wave function in direct space is differentiated by by the partial integral method without using the approximate value of w(k)[50] in space k, the general wave equation in k space by position [74] and approximately the wave function f(x,α) [80] is found. min in space k depending on location and time the exact solution of the wave equations [83,89] and the Taylor Series solution give the wave equation [87,90] in k space according to position and time.

Quantification of chaos in a time series generated from a traffic flow model using the extended entropic chaos degree

Several Traffic flow models have been proposed to address the underlying causes of traffic congestion and develop effective methods for its suppression. Typically, these models are represented using differential or difference equations. Traffic flow models can exhibit chaotic behaviors. Traffic situations become unstable if chaos prevails in the traffic flow. Therefore, the conditions under which chaos occurs in the traffic flow and the intensity of chaos at that time must be investigated. The Lyapunov exponent is used for quantifying the chaos in the dynamical systems. However, computing the Lyapunov exponent over time series without a dynamical map is challenging. Recently, the extended entropic chaos degree has been introduced as an extension of the entropic chaos degree. The extended entropic chaos degree can be computed directly for any time series. Analytically, the extended entropic chaos degree equals the sum of all the Lyapunov exponents for multidimensional non-periodic maps. Moreover, the extended entropic chaos degree is mathematically shown to equal the sum of one positive and one negative Lyapunov exponents for two-dimensional typical chaotic maps, such as a generalized Baker’s map, and a standard map.In this study, we utilize the extended entropic chaos degree to quantify chaos in a time series with less computing complexity compared with conventional methods. First, we demonstrate that chaos can be quantified in a time series generated from a traffic flow model using an extended entropic chaos degree. Lyapunov exponent for a time series of time traveling between two adjacent traffic lights in the traffic flow model is not directly computable because its dynamics are not given as an explicit differential map. Second, we demonstrate that the computational complexity of the extended entropic chaos degree only corresponds to O(M). However, the computational complexities of the estimated Lyapunov exponents using the Wolf method are O(M2), where M is the number of points on a one-dimensional time series. Thus, we show that the extended chaos degree can quantify the chaos in the traffic flow model, such as one vehicle moving through a sequence of traffic lights in one dimension, with less complexity, where the chaos occurs depending on the timing of signal switching.