Frequency Domain Features Research Articles

Bearing life prediction method based on novel health indicators and improved similarity curve matching

Abstract To tackle the issues of low accuracy in similarity curve matching and the challenges in measuring the similarity of degraded curve segments in rolling bearing life prediction methods, we propose a novel bearing remaining useful life (RUL) prediction method utilizing new health indicators and improved similarity curve matching techniques. Initially, time and frequency domain features are extracted from the bearing vibration signals. Subsequently, the multi-dimensional features are meticulously screened using monotonicity, Spearman's correlation, and uncertainty metrics to construct the health indicator (HI) and the health indicator library for the bearing. Subsequently, the support vector data description (SVDD) combined with the PID search algorithm is employed to ascertain the initial prediction time of the bearing and to construct the corresponding health indicator library. Following this, both global and local feature capture are conducted using dual dynamic time warping (DDTW) in conjunction with a sliding window to predict the remaining service life of rolling bearings.
The proposed method is validated on the PHM2012 and XJTU-SY datasets and benchmarked against the latest research. The results demonstrate that our method significantly enhances the prediction accuracy of the remaining useful life of bearings, underscoring the effectiveness and superiority of the proposed approach.and superiority of the proposed approach.

Machine learning-based EEG analysis for enhanced schizophrenia classification and diagnosis

Abstract. Schizophrenia can have a significant impact on patients' lives, studies, and work. Patients may experience delusions, hallucinations, disorganized thinking, and abnormal behavior, among other symptoms. Therefore, research related to schizophrenia is of great importance. This paper proposes the design of a classifier based on machine learning for diagnosing schizophrenia. The classifier extracts N100 features, P300 features, and power features across different frequency bands from both time-domain and frequency-domain characteristics from Electroencephalography (EEG) signals. Given the small dataset, a Support Vector Machine (SVM) machine learning algorithm was chosen to process and classify the selected features. To address the limitations of SVM in handling high-dimensional data and nonlinear problems, this study introduces a comprehensive improvement method based on Bayesian optimization, Recursive Feature Elimination (RFE), and data augmentation. Bayesian optimization was used to find the best combination of hyperparameters for the model, thereby improving its performance; RFE was employed to assess feature importance and remove the least important features, enhancing the model's training efficiency and generalization capability. Additionally, data augmentation was included to increase the sample size and introduce diversity, thereby improving the model's robustness. The study found that these methods effectively improved classification accuracy and generalization ability.

A hybrid local-global neural network for visual classification using raw EEG signals

EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to extract features from raw signals still perform lower than traditional frequency-domain features, and the methods are typically evaluated on small-scale datasets at a low sample rate, which can hinder the capabilities of deep-learning models. To overcome these limitations, we propose a hybrid local-global neural network, which can be trained end-to-end from raw signals without handcrafted features. Specifically, we first propose a reweight module to learn channel weights adaptively. Then, a local feature extraction module is designed to capture basic EEG features. Next, a spatial integration module fuses information from each electrode, and a global feature extraction module integrates overall time-domain characteristics. Additionally, a feature fusion module is proposed to extract efficient features in high sampling rate settings. The proposed model achieves state-of-the-art results on two commonly used small-scale datasets and outperforms baseline methods on three under-studied large-scale datasets. Ablation experimental results demonstrate that the proposed modules have a stable performance improvement ability on multiple datasets across different sample rates, providing a robust end-to-end learning framework.

A Dual-Stream Fusion Network for Human Energy Expenditure Estimation with Wearable Sensor

With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation.

Research on coal rock parameter calibration based on discrete element method

To ensure that the discrete element model of the coal wall accurately reflects the actual cutting process of coal and rock during virtual prototype simulation of mining equipment, this study aims to expedite the development of intelligent mining machinery. Using coal samples from the 4602 working face of Yangcun Coal Mine, operated by Yanzhou Coal Mining Group, we conducted coal rock packing experiments and uniaxial compression tests to obtain the packing angle and compressive strength of the coal samples. Based on the experimental results, we designed Plackett–Burman experiments (PB experiments), steepest ascent experiments, and Box-Behnken experiments to study the influence of particle contact mechanics parameters and bonding mechanics parameters on the packing angle and compressive strength, using the packing angle and compressive strength as response variables. Our objective is to minimize the relative error between the simulated packing angle and the measured packing angle. We solved and optimized the parameter calibration model and conducted simulation calibration experiments based on the optimization results. A comparative analysis with the actual test results revealed that the maximum relative errors between the simulated and measured values for the packing angle and compressive strength were only 2.9% and 5.0%, respectively. Additionally, a discrete element model of a typical working face coal wall was established based on the parameters obtained from this calibration method. A bidirectional coupling model of the cutting process between the coal and rock was created using EDEM-RecurDyn to simulate the rigid-flexible coupling of the coal cutter. An experimental coal wall model was constructed based on similarity theory, and both simulation and physical experiments were conducted. The evaluation metrics for comparison were the time-domain and frequency-domain characteristics of the drum’s vibration signals. The maximum relative error for the time-domain signal characteristics between the two experimental setups was only 4.19%, while the maximum relative error for the frequency-domain signal characteristics was 3.75%. This validates the feasibility of the proposed calibration method for the discrete element coal wall model and the accuracy of the calibration results. Furthermore, it demonstrates that the constructed discrete element coal wall accurately represents the actual coal and rock properties. The virtual simulation based on this model effectively replicates the interaction process between mining machinery and coal, providing a safe, efficient, and low-cost technological approach for performance analysis of mining equipment and intelligent control of supporting devices.

Classification of faults in friction stir processed composites using a machine learning and ensemble learning approach

Abstract Aluminium alloy based surface composites with hard reinforcement particles have wide scope in aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing (FSP) with varying parameters are investigated for the faults occurred during fabrication process. It explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano-powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data (vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories (e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Thenafter, various types of feature selection techniques, viz filter, wrapper and embedded methods are implemented to select most relevant features. The selected subset of features from all selection methods used, are applied to different machine learning and ensemble learning classifiers and their performances are evaluated. The optimal combinations of the type of feature selection method and classifier used, are obtained for efficient classification of surface defects in composited formed by FSP. The real time monitoring and defect detection system can be developed in future for the composites developed by FSP using the developed models.

Exploring EEG-Based Biomarkers for Improved Early Alzheimer's Disease Detection: A Feature-Based Approach Utilizing Machine Learning

This paper presents a comprehensive investigation into Electroencephalogram (EEG) signal processing and analysis techniques aimed at enhancing early diagnosis methods for Alzheimer's Disease (AD). Leveraging a dataset that has EEG data of individuals diagnosed with Mild Cognitive Impairment (MCI), AD, Healthy Controls, and the study explores Preprocessing Methods and Feature Extraction Techniques, with machine learning model notably Support Vector Machines (SVM).In the preprocessing phase, a combination of high pass, lowpass, Savitzky–Golay, and median filters are applied, informed by a comprehensive review of filter comparison literature. Feature extraction encompasses three primary categories: ‘Statistical, ‘Frequency Domain’ and ‘Time Domain’. The scope of this work is to explore features in all these three domains and build SVM based model for efficient classification. In our investigation, we achieved a categorization accuracy of 92% through the utilization of statistical features. Employing time domain features resulted in an accuracy of 87%, while frequency domain features also yielded an 87% accuracy rate in our study. The primary objective of this study is that it aims to enhance early AD diagnosis through advanced EEG signal processing and machine learning techniques, focusing on preprocessing methods, feature extraction, and classification accuracy.

Experimental investigation on temperature distribution characteristics inside the condensing tube of the R134a two-phase natural circulation loop

Temperature distribution of two-phase flow inside the condensing tube can effectively reflect the heat transfer characteristic of the fluid, playing a pivotal role in the study of heat transfer in two-phase flow systems. In this study, we use the stainless steel capillary sealed distributed fiber Bragg grating (S-DFBGs) to measure and characterize the temperature distribution of R134a fluid inside the condensing tube under steady-state (pump-driven two-phase circulation) and unsteady-state (type I and II density wave instabilities in two-phase natural circulation loop) conditions, and the frequency and time domain characteristics of the fluid temperature distribution inside the tube has been investigated in detail. Through frequency-domain analysis, the temperature spectrum inside the condensing tube can indentify two types of the instability together with flowrate and pressure. Through time-domain analysis, temperature distribution inside the condenser can dermine the phase transition point, hence the condensation length. Moreover, two types of the instability exhibit different temperature fluctuation characteristics, providing reliable and effective information for further research on two phase natural circulation instability mechanisms.

Recognition of Heat-Damaged Corn Seeds Based on Fusion of Laser Ultrasonic Signal and Infrared Image Features

Corn is widely cultivated on a global scale. However, high temperatures during storage and transportation can lead to thermal damage to the kernels, negatively impacting their quality. Traditional methods for identifying heat-damaged grains primarily rely on manual inspection, which is characterized by low efficiency and accuracy. This study proposes a novel identification method that integrates laser ultrasonic signals with infrared image texture features. A pulsed laser stimulates the seeds to generate laser ultrasonic signals, while an infrared camera captures infrared images of the seeds. We extract time-domain, frequency-domain, and Hilbert-domain features from the laser ultrasonic signals, in addition to texture features from the infrared images. These features are combined using Canonical Correlation Analysis (CCA). Subsequently, the fused features are classified using a Backpropagation (BP) neural network, Support Vector Machine (SVM), and Particle Swarm Optimization–Support Vector Machine (PSO–SVM). The results indicate that the recognition rate achieved with the fused ‘signal-image’ features reaches 99.17%, providing a novel approach for detecting heat-damaged corn seeds.

Living-Skin Detection Based on Spatio-Temporal Analysis of Structured Light Pattern.

Living-skin detection is an important step for imaging photoplethysmography and biometric anti-spoofing. In this paper, we propose a new approach that exploits spatio-temporal characteristics of structured light patterns projected on the skin surface for living-skin detection. We observed that due to the interactions between laser photons and tissues inside a multi-layer skin structure, the frequency-domain sharpness feature of laser spots on skin and non-skin surfaces exhibits clear difference. Additionally, the subtle physiological motion of living-skin causes laser interference, leading to brightness fluctuations of laser spots projected on the skin surface. Based on these two observations, we designed a new living-skin detection algorithm to distinguish skin from non-skin using spatio-temporal features of structured laser spots. Experiments in the dark chamber and Neonatal Intensive Care Unit (NICU) demonstrated that the proposed setup and method performed well, achieving a precision of 85.32%, recall of 83.87%, and F1-score of 83.03% averaged over these two scenes. Compared to the approach that only leverages the property of multilayer skin structure, the hybrid approach obtains an averaged improvement of 8.18% in precision, 3.93% in recall, and 8.64% in F1-score. These results validate the efficacy of using frequency domain sharpness and brightness fluctuations to augment the features of living-skin tissues irradiated by structured light, providing a solid basis for structured light based physiological imaging.

Determination of grout compactness in rebar grouted splice sleeves using frequency domain characteristics of the excitation process

Grouted splice sleeves are widely used in assembly building connections. However, void defects within the sleeve directly affect the load-bearing capacity of the building structure. This paper presents a new method for detecting void defects inside a grouted splice sleeve based on the frequency domain characteristics of mechanical vibration waves. This method analyses the grouted spliced casing containing void defects as a gas-liquid two-phase flow system. To confirm the validity of the method, this paper builds a test setup for testing. The results showed that the test method can effectively detect the void defects and the volume of the defects inside the grouted splice sleeves when the grout is not solidified.

Transfer learning and source domain restructuring-based BiLSTM approach for building energy consumption prediction

ABSTRACT Currently, building energy consumption prediction typically relies on vast amounts of historical data. However, for newly constructed buildings, the scarcity of data leads to reduced prediction accuracy. To address this challenge, this paper proposes a novel approach that integrates transfer learning with a source domain reconstruction-based BiLSTM model for building energy consumption prediction. In the first stage, both source and target domains are clustered into profile types using k-means. For each profile type in the target domain, the most similar profiles in the source domain are identified using Maximum Mean Discrepancy and Dynamic Time Warping. The source domain is then reconstructed by combining these identified profiles based on their proportions in the target domain. Subsequently, a feature extraction method based on EMD-CWT-Conv is introduced. Empirical Mode Decomposition is first applied to decompose and filter the source domain data. Continuous Wavelet Transform is then employed to extract distinctive frequency-domain and time-domain features from both the reconstructed source domain and the target domain. Final predictions are made using transfer learning and fine-tuning. Experiments on a grocery shop and a school show that the proposed approach reduces Mean Absolute Percentage Error by at least 13.19% and 17.67%, respectively.

A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings

Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate the implementation of effective preventive maintenance strategies. This article proposed a novel RUL-centric data augmentation method, designated as DF-MDAGRU, for the purpose of predicting the remaining useful life (RUL) of bearings. This model is based on an encoder–decoder framework that integrates time–frequency domain feature enhancement with multidimensional dynamic attention gated recurrent units for feature extraction. This method enhances time–frequency domain features through the Discrete Wavelet Downsampling module (DWD) and Convolutional Fourier Residual Block (CFRB). This method employs a Multiscale Channel Attention Module (MS-CAM) and a Multiscale Convolutional Spatial Attention Mechanism (MSSAM) to extract channel and spatial feature information. Finally, the output predictions are processed through linear regression to achieve the final RUL estimation. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches on the FEMETO-ST and XJTU datasets.

Study on the influence mechanism of gate oxide degradation on DM EMI signals in SiC MOSFET

This paper investigates gate oxide degradation and its influence on Differential Mode(DM) EMI signals. The study reveals that changes in the parasitic capacitance within the chip, resulting from gate oxide degradation, can modify the amplitude-frequency characteristics of DM EMI signals, leading to unexpected transmission outcomes. This paper subjects SiC MOSFET modules to periodic high-temperature gate bias stress, extracts the spectrum characteristics of DM EMI, and assesses the impact of junction temperature. The analysis explores the impact of gate oxide degradation on low and high frequency DM EMI signals. Experimental results reveal distinct sensitivities of amplitude-frequency characteristics in the DM EMI signals to temperature variations and responses to gate oxide degradation at various frequency bands. The extrapolation of the correlation between the frequency-domain characteristics of the DM EMI signals and the degree of gate oxide degradation introduces a novel approach to evaluate the lifespan of power electronic devices.

Enhanced Hessian locally linear embedding method for rolling bearing fault diagnosis based upon novel variational mode decomposition

Due to the plenty of redundant information contained in the fault feature sets of the original vibration signals of rolling bearings, the diagnostic accuracy is reduced and computational complexity is increased, making it very difficult to achieve accurate and fast fault recognition. So as to address the hard issue, this article develops an Enhanced Hessian Locally Linear Embedding (EHLLE) method for rolling bearing fault diagnosis based upon Novel Variational Mode Decomposition (NVMD). Firstly, NVMD method is utilized to denoise sample vibration signals and the sensitive modes containing main fault information are obtained. Secondly, some time-domain and frequency-domain statistical features are used to construct sample high-dimensional features, the dimensionality of which is reduced by EHLLE. Ultimately, K-Means algorithm is employed to cluster the low-dimensional features, which are utilized to train Support Vector Machine (SVM), and then the trained SVM is applied to the recognition of testing sample low-dimensional features. Two experiments of rolling bearing fault diagnosis demonstrate the effectiveness of NVMD-EHLLE method. Additionally, the superiority of the developed method is verified by comparing with other three typical methods.

Performance analysis of DC-DC Buck converter with innovative multi-stage PIDn(1+PD) controller using GEO algorithm

Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices. In a DC-DC converter, steady-state performance with minimum error and fast dynamic response relies on controller design. This paper presents the design of a multi-stage PID controller with an N-filter combined with a one plus proportional derivative (1+PD) controller. This controller illustrates fast tracking reference voltage; additionally, it shows incredible results when the DC-DC converter operates in different modes. The parameters of the proposed controller are effectively determined using the golden eagle optimization (GEO) algorithm. Furthermore, a comprehensive comparison between the proposed controller, proportional–integral–derivative (PID), and fractional order PID (FOPID) controllers, as well as different metaheuristic optimization methods in various conditions, has been conducted to demonstrate the effectiveness of the proposed controller. The behavior of the closed-loop system under different conditions has been thoroughly investigated. The superior time and frequency domain characteristics of the closed-loop system with the PIDn(1+PD) controller highlight its superiority over other controllers. The demonstrated enhancements in settling time, voltage regulation accuracy, and transient response emphasize the potential applicability of the proposed control strategy in real-world power electronics systems, particularly in scenarios requiring high efficiency, stability, and dynamic performance.

Tool wear prediction based on K-means and Adaboost auto-encoder

Abstract A new tool wear prediction model is proposed to address the tool wear issue, aimed at monitoring tool wear based on specific task requirements and guiding tool replacement during actual cutting operations. In the data preprocessing phase, tool wear states are classified using unsupervised K-means clustering. The time, frequency, and time-frequency domain features are then labeled and fused using an autoencoder neural network applied to the original set of signal features from the tool. For tool wear prediction, an enhanced autoencoder neural network leveraging AdaBoost is employed to establish the prediction model. The reconstruction error serves as the chosen loss function to assess the autoencoder's performance, taking into account data correlation and the inherent lossy nature of the autoencoder. Experimental results from real machining data obtained from a CNC milling machine demonstrate that the proposed model achieves higher prediction accuracy while reducing data dimensions.

Display Field Communication: Enabling Seamless Data Exchange in Screen–Camera Environments

Display field communication (DFC) is an emerging technology that enables seamless communication between electronic displays and cameras. It utilizes the frequency-domain characteristics of image frames to embed and transmit data, which are then decoded and interpreted by a camera. DFC offers a novel solution for screen-to-camera data communication, leveraging existing displays and camera infrastructures. This makes it a cost-effective and easily deployable solution. DFC can be applied in various fields, including secure data transfer, mobile payments, and interactive advertising, where data can be exchanged by simply pointing a camera at a screen. This article provides a comprehensive survey of DFC, highlighting significant milestones achieved in recent years and discussing future challenges in establishing a fully functional DFC system. We begin by introducing the broader topic of screen–camera communication (SCC), classifying it into visible and hidden SCC. DFC, a type of spectral-domain hidden SCC, is then explored in detail. Various DFC variants are introduced, with a focus on the physical layer. Finally, we present promising experimental results from our lab and outline further research directions and challenges.

A novel monitoring method based on the fusion of sound and image signals for laser welding penetration status

Accurately identifying penetration status is crucial for ensuring high-quality welding. However, relying on a single sensor to monitor the welding status falls short in accuracy. Thus, a novel monitoring method that fuses sound and image is proposed for assessing the laser welding penetration status. First, a microphone and a high-speed camera are employed to collect sound and image signals during welding. Second, to overcome noise challenges in sound signal, a denoising method is proposed that utilizes Grey Wolf Optimization (GWO) to find the optimal parameters for Variational Mode Decomposition (VMD). The time and frequency domain features from the denoised sound and Local Binary Pattern (LBP) features from the image are extracted. These features constitute a high-dimensional dataset, which is reduced using Principal Component Analysis (PCA). Finally, a back propagation neural network (BPNN) is constructed for fusion monitoring. The results demonstrate that fusion monitoring achieves higher accuracy (97.9%) compared to using sound (79.9%) or image (92.3%) features independently. In comparison to other methods like K-Nearest Neighbor (KNN), Naive Bayesian (NB), Support Vector Machine (SVM), and Decision Tree, BPNN fusion monitoring achieves higher accuracies, with increases of 18.09%, 12.79%, 6.53%, and 25.84% respectively. The proposed method helps to promote the development of welding intelligence.

Multi-source information fused loose particle localization and material identification method for sealed electronic equipment

Sealed electronic equipment are1 an important component of aerospace defense systems, and loose particles pose a significant threat to their reliable operation. Loose particle detection is crucial. For sealed electronic equipment with large scale and complex structure, loose particle detection should not only include the judgment of existence, but also obtain location and material information to facilitate the cleaning and control work. In this paper, the authors proposed a multi-source information fused loose particle localization and material identification method. Firstly, the equipment model was designed, the loose particle samples were made, and loose particle signals were collected. Secondly, the two-stage adaptive energy threshold pulse extraction algorithm was newly proposed to extract effective pulses, and the threshold-judgement-search pulse matching algorithm was improved to match the effective pulse groups. Next, spectrograms were transformed from effective pulses to create the localization and material image set. The time-domain, frequency-domain and gray-level co-occurrence matrix features were used to construct the joint feature library. Then, the channel-weighting feature selection method was used to create the localization and material data set. Finally, PReLU-VGG19-Plus was trained on the localization and material image set to obtain the optimal localization and material neural network, while parameter-optimized XGBoost was trained on the localization and material data set to obtain the optimal localization and material classifier. On this basis, combined with the triple majority voting process, the combined localization and material framework were constructed. Extensive test results show that, the location-identification achieved by combined localization framework and the material-identification accuracy achieved by combined material framework are all 100%. The feasibility, stability, and superiority of the method proposed in this paper have been fully verified. It is an important supplement to the existing loose particle detection research, providing important reference for signal detection and classification research in similar fields, and effectively improving the reliability of sealed electronic equipment.