scholarly journals Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bin Li ◽  
Le Kui ◽  
Jingdong Luo ◽  
Shiyong Chen
Keyword(s):  
Feature Extraction ◽  
Confidence Interval ◽  
Life Prediction ◽  
Amplitude Spectrum ◽  
Feature Selection Method ◽  
Vibration Signal ◽  
Support Vector ◽  
Mechanical Equipment ◽  
Double Row ◽  
Svm Model

Mechanical equipment is a key component of mechanical equipment, and its working condition is directly related to the overall performance of mechanical equipment. Accurate evaluation and prediction of the performance degradation trend of mechanical equipment is of great significance to ensure the reliability and safety of the mechanical equipment system. Based on the data of typical faulty equipment, this paper analyzes the energy characteristic parameters of mechanical equipment under different types and degrees of failure in the time domain. Using amplitude spectrum analysis, Hilbert envelope demodulation and wavelet packet decomposition method, and other vibration signal processing methods, preliminary extraction of multiple statistical feature parameters are given. Secondly, in view of the irrelevant and redundant components of multiple statistical parameters, a new method for extracting fault features of mechanical equipment based on variance value and principal component analysis is proposed. This method can effectively classify the fault status of mechanical equipment. The effectiveness of the method is verified by actual equipment signals. After that, the value extracted from the vibration signal of the double-row roller equipment is used as the degradation feature. In order to reduce the influence of irregular characteristics in the vibration signal and simplify the complexity of the vibration signal, the wavelet transform and the support vector machine model are combined, according to the degradation after decomposition. The 95% confidence interval of the predicted value is also given. The SVM model is established based on data characteristics, and single-step and multistep prediction of equipment degradation trends are carried out. The prediction result shows that, according to the mapping position formula, the distribution of equipment degradation prediction points is obtained, and a 95% confidence interval based on the distribution of the prediction points is given. Finally, on the basis of completing feature extraction, this paper applies an unsupervised feature selection method. The sensitive characteristics of life prediction and the prediction results of a single SVM model and a neural network model are compared and analyzed at the same time.

Design of Text Categorization System Based on SVM

2012 ◽  
Vol 532-533 ◽  
pp. 1191-1195 ◽  
Author(s):  
Zhen Yan Liu ◽  
Wei Ping Wang ◽  
Yong Wang
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Support Vector ◽  
Text Representation ◽  
Text Feature ◽  
Categorization System ◽  
Classifier Training

This paper introduces the design of a text categorization system based on Support Vector Machine (SVM). It analyzes the high dimensional characteristic of text data, the reason why SVM is suitable for text categorization. According to system data flow this system is constructed. This system consists of three subsystems which are text representation, classifier training and text classification. The core of this system is the classifier training, but text representation directly influences the currency of classifier and the performance of the system. Text feature vector space can be built by different kinds of feature selection and feature extraction methods. No research can indicate which one is the best method, so many feature selection and feature extraction methods are all developed in this system. For a specific classification task every feature selection method and every feature extraction method will be tested, and then a set of the best methods will be adopted.

scholarly journals NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3147 ◽  
Author(s):  
Liu Zhang ◽  
Zhenhong Rao ◽  
Haiyan Ji
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Support Vector ◽  
Multivariate Methods ◽  
Model Combination ◽  
Modeling Analysis ◽  
Svm Model ◽  
Wheat Kernels

In this study, a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky–Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC–NCA–SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC–NCA–SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.

SVM MODELS FOR DIAGNOSING BALANCE PROBLEMS USING STATISTICAL FEATURES OF THE MTC SIGNAL

2008 ◽  
Vol 07 (03) ◽  
pp. 317-331 ◽  
Author(s):  
DANIEL T. H. LAI ◽  
REZAUL BEGG ◽  
MARIMUTHU PALANISWAMI
Keyword(s):  
Feature Selection Method ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Model Parameters ◽  
Statistical Features ◽  
Gait Patterns ◽  
Healthy Elderly ◽  
Svm Model ◽  
Balance Problems

Trip-related falls are a major problem in the elderly population and research in the area has received much attention recently. The focus has been on devising ways of identifying individuals at risk of sustaining such falls. The main aim of this work is to explore the effectiveness of models based on Support Vector Machines (SVMs) for the automated recognition of gait patterns that exhibit falling behavior. Minimum toe clearance (MTC) during continuous walking on a treadmill was recorded on 10 healthy elderly and 10 elderly with balance problems and with a history of tripping falls. Statistical features obtained from MTC histograms were used as inputs to the SVM model to classify between the healthy and balance-impaired subjects. The leave-one-out technique was utilized for training the SVM model in order to find the optimal model parameters. Tests were conducted with various kernels (linear, Gaussian and polynomial) and with a change in the regularization parameter, C, in an effort to identify the optimum model for this gait data. The receiver operating characteristic (ROC) plots of sensitivity and specificity were further used to evaluate the diagnostic performance of the model. The maximum accuracy was found to be 90% using a Gaussian kernel with σ2 = 10 and the maximum ROC area 0.98 (80% sensitivity and 100% specificity), when all statistical features were used by the SVM models to diagnose gait patterns of healthy and balance-impaired individuals. This accuracy was further improved by using a feature selection method in order to reduce the effect of redundant features. It was found that two features (standard deviation and maximum value) were adequate to give an improved accuracy of 95% (90% sensitivity and 100% specificity) using a polynomial kernel of degree 2. These preliminary results are encouraging and could be useful not only for diagnostic applications but also for evaluating improvements in gait function in the clinical/rehabilitation contexts.

Feature Extraction of Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 993-997
Author(s):  
Li Jie Sun ◽  
Li Zhang ◽  
Yong Bo Yang ◽  
Da Bo Zhang ◽  
Li Chun Wu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Attribute Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Characteristic Parameters ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

scholarly journals Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668308 ◽  
Author(s):  
Shuangyuan Wang ◽  
Yixiang Huang ◽  
Liang Gong ◽  
Lin Li ◽  
Chengliang Liu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Coding ◽  
Extraction Procedure ◽  
Vibration Signal ◽  
Support Vector ◽  
Data Set ◽  
Vibration Signals ◽  
Efficiency And Effectiveness ◽  
Gear Fault

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

scholarly journals Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1790
Author(s):  
Zi Zhang ◽  
Hong Pan ◽  
Xingyu Wang ◽  
Zhibin Lin
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Damage Detection ◽  
Lamb Wave ◽  
Lamb Waves ◽  
Feature Selection Method ◽  
Support Vector ◽  
Time Frequency ◽  
Damage States

Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped.

scholarly journals Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

2019 ◽  
Vol 10 ◽  
pp. 13 ◽  
Author(s):  
Long Li ◽  
Jianfeng Xiao ◽  
Bin Wu ◽  
Mengge Zhou ◽  
Qian Wang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Extraction Method ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Normal Operation ◽  
Support Vector ◽  
Feature Extraction Method ◽  
High Voltage Circuit Breaker

The development of power grid system not only increases voltage and capacity, but also increases power risk. This paper briefly introduces the feature extraction method of the vibration signal of high voltage circuit breaker and support vector machine (SVM) algorithm and then analyzed the high voltage circuit breaker in three states: normal operation, fixed screw loosening and falling of opening spring, using the SVM based on the above feature extraction method. The results showed that the accuracy and precision rates of fault identification of circuit breaker were the highest by using the wavelet packet energy entropy extraction features, the false alarm rate was the lowest, and the detection time was the shortest.

Application of Support Vector Machine for Arabic Sentiment Classification Using Twitter-Based Dataset

2020 ◽  
Vol 19 (01) ◽  
pp. 2040018
Author(s):  
Sarah N. Alyami ◽  
Sunday O. Olatunji
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Saudi Arabia ◽  
Classification Accuracy ◽  
Social Issues ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Model ◽  
Vision 2030 ◽  
Arabic Sentiment Analysis

Sentiment classification is the process of classifying emotions and opinions in texts. In this study, the problem of Arabic sentiment analysis was addressed. A support vector machine (SVM) model was proposed to classify opinions in Arabic micro-texts as being positive or negative. To evaluate the performance of the SVM model, a dataset was built from tweets discussing several social issues in Saudi Arabia. These issues include changes that were implemented by the country as part of a newly established vision, known as Saudi Arabia Vision 2030. The constructed dataset was manually annotated according to the sentiment conveyed in the text. To achieve the best sentiment classification accuracy, several procedures were implemented within the proposed framework including light stemming, feature extraction (Ngrams, emoji and tweet-topic features), parameter optimisation and feature-set reduction. The experimental results revealed excellent outcomes. An accuracy of 89.83% was achieved using the proposed SVM model.

scholarly journals Fault Diagnosis Method for Hydraulic Pump Based on Fuzzy Entropy of Wavelet Packet and LLTSA

2018 ◽  
Vol 14 (02) ◽  
pp. 60
Author(s):  
Wang Fei ◽  
Fang Liqing ◽  
Qi Ziyuan
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Classification Performance ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
Hydraulic Pump ◽  
Feature Extraction Method ◽  
Diagnosis Method ◽  
New Feature

<p>As the vibration signal <a href="app:ds:characteristic" target="_self">characteristic</a>s of hydraulic pump <a href="app:ds:present%20(a%20certain%20appearance)" target="_self">present</a> non-stationary and the fault features is difficult to extract, a new feature extraction method was proposed .This approach combines wavelet packet analysis techniques, fuzzy entropy and LLTSA (liner local tangent space alignment) which is one of typical manifold learning methods to <a href="app:ds:extract" target="_self">extract</a>ing  <a href="app:ds:fault" target="_self">fault</a>  feature. Firstly, the vibration signals were decomposed into eight signals in different <a href="app:ds:scale" target="_self">scale</a>s, then the fuzzy entropies of signals were calculated to constitute eight <a href="app:ds:many%20dimensions" target="_self">dimensions</a> <a href="app:ds:feature" target="_self">feature</a> vector. Secondly, LLTSA method was applied to compress the high-dimension features into low-dimension features which have a better classification performance. Finally, the SVM (support vector machine) was employed to <a href="app:ds:distinguish" target="_self">distinguish</a> different <a href="app:ds:fault" target="_self">fault</a> features. Experiment results of hydraulic pump feature extraction show that the proposed method can exactly classify different fault type of hydraulic pump and this method has a significant advantage <a href="app:ds:compare" target="_self">compare</a>d with other feature extraction means mentioned in this paper.</p><p> </p>

scholarly journals A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Baiyan Chen ◽  
Hongru Li ◽  
He Yu ◽  
Yukui Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Vibration Signal ◽  
Support Vector ◽  
Characteristic Matrix ◽  
Evaluation Technique ◽  
Feature Extraction Method ◽  
Hybrid Domain ◽  
Degradation State ◽  
Motor Bearing

The vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance evaluation technique (DET) is proposed to solve this problem. Firstly, the vibration signal of the motor bearing is decomposed by ensemble empirical mode decomposition (EEMD). The proper intrinsic mode function (IMF) component that is the most sensitive to the degradation of the motor bearing is selected according to the sensitive IMF selection algorithm based on the similarity evaluation. Then the distance evaluation factor of each characteristic parameter is calculated by the DET method. The differential method is used to extract sensitive characteristic parameters which compose the characteristic matrix. And then the extracted degradation characteristic matrix is used as the input of support vector machine (SVM) to identify the degradation state. Finally, It is demonstrated that the proposed hybrid domain feature extraction method has higher recognition accuracy and shorter recognition time by comparative analysis. The positive performance of the method is verified.

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