Debonding Analysis and Identification of the Interface Between Sleeper and Track Slab for Twin-Block Slab Tracks

2021 ◽  
pp. 2140011
Author(s):  
Wei Du ◽  
Shi-Jie Deng ◽  
Juan-Juan Ren ◽  
Ze-Ming Zhao ◽  
Zhen Wei ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Interface Debonding ◽  
Lateral Side ◽  
Zone Model ◽  
Vertical Acceleration ◽  
Twin Block ◽  
Time Frequency ◽  
Track Slab

For China Railway Track System (CRTS) I twin-block slab tracks, the interface between the sleeper and track slab is susceptible to damage under the coupled effect of long-term train load and external environment factors. In order to analyze the damage behavior and identify the type of debonding at the interface, this paper established a three-dimensional finite element model and introduced the cohesion zone model and concrete damaged plasticity model to simulate the interface damage and the inner-layer damage of the track slab, respectively. The interface debonding induced by the temperature effect was analyzed, and the debonding types were identified based on the obtained vertical vibration responses of the sleeper surface under the train load. The results reveal that the damage mainly occurs on the bottom and lateral sides at the interface under the temperature load. The track model can be refined further to obtain higher analysis accuracy with acceptable calculation time using the sequential loading method. The 26 damage features derived from the time domain, frequency domain, and time–frequency domain are in good representativeness in reflecting the damage information hidden in the vibration signals. Among them, the peak values (maximum vertical acceleration of the sleeper) are 55.0, 56.7, 60.3, and 61.6[Formula: see text]m/s2 for no debonding, debonding on the lateral side, debonding at the bottom, and debonding on the longitudinal side of the interface under train load, respectively. Moreover, the identification accuracy of the debonding type can reach 93.75% combining the particle swarm algorithm and support vector machine. It indicates that the proposed identification method is effective and reliable to provide theoretical guidance for developing scientific maintenance and repair strategies for twin-block slab tracks.

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 574-577
Author(s):  
Lu Zhang ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Xiao Liang Feng
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Frequency Domain ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Machining Processes ◽  
Time Frequency ◽  
Svm Model ◽  
The Status ◽  
Wear Monitoring

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method to predict tool wear status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force features to indicate the status of tool wear. Meanwhile, a support vector machine (SVM) model is employed to distinguish the tool wear status. The result of the classification of different tool wear status proved that features extracted from time-frequency domain can be the recognize-features of high recognition precision.

scholarly journals EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2739 ◽  
Author(s):  
Rami Alazrai ◽  
Rasha Homoud ◽  
Hisham Alwanni ◽  
Mohammad Daoud
Keyword(s):  
Emotion Recognition ◽  
Frequency Domain ◽  
Frequency Distribution ◽  
Support Vector ◽  
Eeg Signals ◽  
Time Frequency ◽  
Labeling Schemes ◽  
Frequency Representation ◽  
Frequency Features ◽  
Time Frequency Distribution

Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73 . 8 % – 86 . 2 % . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

scholarly journals Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3496
Author(s):  
Jiacan Xu ◽  
Hao Zheng ◽  
Jianhui Wang ◽  
Donglin Li ◽  
Xiaoke Fang
Keyword(s):  
Frequency Domain ◽  
Motor Imagery ◽  
Recognition Performance ◽  
Feature Learning ◽  
Feature Representation ◽  
Support Vector ◽  
Imagery Task ◽  
Learning Method ◽  
Eeg Signals ◽  
Time Frequency

Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.

Analysis of Time, Frequency and Wavelet Based Features of Vibration and Current Signals for Fault Diagnosis of Induction Motors Using SVM

2017 ◽  
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Induction Motors ◽  
Classification Performance ◽  
Operating Conditions ◽  
Support Vector ◽  
Time Frequency ◽  
Mechanical Faults ◽  
Severity Levels ◽  
Electrical Faults

This paper presents a comparative analysis of the time, frequency and time-frequency domain based features of the vibration and current signals for identifying various faults in induction motors (IMs) using support vector machine (SVM). Four mechanical faults (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor), and three electrical faults (broken rotor bars, stator winding fault with two severity levels and phase unbalance with two severity levels) are considered in the present study. The proposed fault diagnosis consists of three steps. In the first step, the vibration in three orthogonal directions and the current in three phases are acquired from the healthy and faulty motors using a machine fault simulator (MFS). In second step, useful statistical features are extracted from the time, frequency and time-frequency domain (continuous wavelet transform (CWT)) of the signal. For the effective fault diagnosis, SVM parameters are optimally selected based on the grid-search method along with 5-fold cross-validation, and the effective fault features are selected based on the wrapper model. Finally, the fault diagnosis of IM is performed using optimal SVM parameters and effective features as input to the SVM. The classification performance of all methodologies developed in three domains is compared for various operating conditions of IMs. The test results showed that the developed methodology could isolate ten IM fault conditions successfully based on features from all three domains at all IM operating conditions; however, time-frequency features give the best results.

scholarly journals An Intelligence Method for Recognizing Multiple Defects in Rail

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8108
Author(s):  
Fei Deng ◽  
Shu-Qing Li ◽  
Xi-Ran Zhang ◽  
Lin Zhao ◽  
Ji-Bing Huang ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Guided Waves ◽  
Single Point ◽  
Guided Wave ◽  
Support Vector ◽  
Time Frequency ◽  
Fault Recognition ◽  
Different Types ◽  
Defect Recognition ◽  
The Time Domain

Ultrasonic guided waves are sensitive to many different types of defects and have been studied for defect recognition in rail. However, most fault recognition algorithms need to extract features from the time domain, frequency domain, or time-frequency domain based on experience or professional knowledge. This paper proposes a new method for identifying many different types of rail defects. The segment principal components analysis (S-PCA) is developed to extract characteristics from signals collected by sensors located at different positions. Then, the Support Vector Machine (SVM) model is used to identify different defects depending on the features extracted. Combining simulations and experiments of the rails with different kinds of defects are established to verify the effectiveness of the proposed defect identification techniques, such as crack, corrosion, and transverse crack under the shelling. There are nine channels of the excitation-reception to acquire guided wave detection signals. The results show that the defect classification accuracy rates are 96.29% and 96.15% for combining multiple signals, such as the method of single-point excitation and multi-point reception, or the method of multi-point excitation and reception at a single point.

scholarly journals Bearing Fault Diagnosis in the Mixed Domain Based on Crossover-Mutation Chaotic Particle Swarm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tongle Xu ◽  
Junqing Ji ◽  
Xiaojia Kong ◽  
Fanghao Zou ◽  
Wilson Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Particle Swarm ◽  
Diagnostic Technique ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Bearing Fault ◽  
Mutation Strategy

The classification frameworks for fault diagnosis of rolling element bearings in rotating machinery are mostly based on analysis in a single time-frequency domain, where sensitive features are not completely extracted. To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency domain. Secondly, the weighted maximum relevance minimum redundancy (WMRMR) algorithm is adopted to reduce the dimension of the feature set and to establish the representative feature set. Thirdly, a new crossover-mutation strategy is suggested to reduce the local minima in optimization, and an optimization disturbance is added. Finally, the support vector machine is optimized using the improved chaotic particle swarm to improve fault classification diagnosis. The effectiveness of the proposed new bearing fault diagnostic technique is verified by experimental tests under different bearing conditions. Test results showed that the bearing fault classification accuracy of CMCPSO-SVM in the mixed domain was much higher than those in a single feature domain.

scholarly journals Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 56 ◽  
Author(s):  
Yancai Xiao ◽  
Zhe Hua
Keyword(s):  
Frequency Domain ◽  
Wind Turbines ◽  
Working Environment ◽  
Fault Prediction ◽  
Support Vector ◽  
Forecasting Model ◽  
Term Operation ◽  
Time Frequency ◽  
Combined Forecasting ◽  
Misalignment Fault

Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine’s vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models.

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