An intelligent fault diagnosis method based on curve segmentation and SVM for rail transit turnout

2021 ◽  
pp. 1-11
Author(s):  
Wenjiang Ji ◽  
Cheng Chen ◽  
Guo Xie ◽  
Lei Zhu ◽  
Yichuan Wang ◽  
...  
Keyword(s):  
Intelligent Transportation System ◽  
Support Vector ◽  
Data Sets ◽  
Health States ◽  
The Real ◽  
Svm Algorithm ◽  
Segmentation Approach ◽  
Daily Task ◽  
Diagnosis Method ◽  
Railway Turnout

With the development of intelligent transportation system, the maintenance of railway turnout is an essential daily task which was required to be efficiency and automatically. This paper presents an intelligent diagnosis method based on deep learning curve segmentation and the Support Vector Machine. Firstly, we studied the curve segmentation approach of the real-time monitoring power data collected form turnout, for which is an essential step and do a great help to improve the diagnose accuracy. Then based on the well pre-processed data sets, the SVM algorithm was applied to classify the samples and report the health states of the turnout which under testing. At last, the experiments were taken on the power data curve collected from the real turnouts, during which we compared the new diagnose method with conventional ones, and the results showed that the diagnose accuracy of proposed method can averaged to 98.5%. Compared with traditional SVM based frameworks, the proposed diagnosis method dramatically improves the accuracy which is more suitable for railway turnout.

scholarly journals A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3105 ◽  
Author(s):  
Cong Dai Nguyen ◽  
Alexander Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Reference Signal ◽  
Vibration Signal ◽  
Support Vector ◽  
Health States ◽  
Informative Signal ◽  
Vibration Signals ◽  
Optimal Output ◽  
Gear Fault ◽  
Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

FAULT DIAGNOSIS BASED ON LMD-SVD AND INFORMATION-GEOMETRIC SUPPORT VECTOR MACHINE FOR HYDRAULIC PUMPS

2015 ◽  
Vol 39 (3) ◽  
pp. 569-580 ◽  
Author(s):  
Ye Tian ◽  
Chen Lu ◽  
Zhipeng Wang ◽  
Zili Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Small Sample ◽  
Support Vector ◽  
Svm Classifier ◽  
Health States ◽  
Diagnosis Method ◽  
Stationary Vibration ◽  
Local Mean ◽  
Value Decomposition

This study proposes a fault diagnosis method for hydraulic pumps based on local mean decomposition (LMD), singular value decomposition (SVD), and information-geometric support vector machine (IG-SVM). First, the nonlinear and non-stationary vibration signals are decomposed using LMD into several product functions (PFs). Then, the PFs are processed by SVD to obtain more stable and compact feature vectors. Finally, the health states are identified by an IG-SVM classifier, which is less-dependent on the selected kernel function and parameters than SVM. In addition, the comparisons between LMD, EMD, and WPD demonstrate the superiority of LMD in feature extraction. Compared with SVM and BP neural network, IG-SVM shows higher classification accuracy and computational efficiency in dealing with small-sample fault diagnosis. From the experimental results, it was concluded that the proposed method can effectively realize fault diagnosis for hydraulic pumps under small-sample conditions.

Convolutional neural network for gear fault diagnosis based on signal segmentation approach

2018 ◽  
Vol 18 (5-6) ◽  
pp. 1401-1415 ◽  
Author(s):  
Seokgoo Kim ◽  
Joo-Ho Choi
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Fault Location ◽  
Data Sets ◽  
The Real ◽  
Signal Segmentation ◽  
Gear Fault ◽  
Segmentation Approach

In the gear fault diagnostics, conventional methods have largely depended on the complicated signal processing and feature extraction skills, which are often cumbersome for engineers to implement easily. Recently, the convolutional neural networks, which is a kind of deep learning techniques, have found increased success in this field by taking advantage of minimal engagement of signal processing and automated features extraction for the fault diagnosis. In the previous studies, however, the accuracy of the method was often assessed by means of cross-validation from the data sets at the same faulted tooth, which may not be the case in the real gear, since the fault may appear at different teeth from the training. This article proposes a convolutional neural network method based on the signal segmentation to solve this problem, which is to divide the original signal into those at each tooth of the gear. The effectiveness of the method is validated by the data made from the gearbox test rig, in which the vibration and transmission errors are measured, respectively. The training and test data sets are prepared at different fault locations. The performances of the convolutional neural network with signal segmentation are compared and discussed with those by the ordinary convolutional neural network without segmentation. As a whole, the results with the signal segmentation suggest that the fault can be successfully identified even when the fault location in the test is different from that of the training, which provides great feasibility toward the real applications.

scholarly journals Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb

2021 ◽  
Vol 5 (1) ◽  
pp. 11-20
Author(s):  
Wahyu Hidayat ◽  
◽  
Mursyid Ardiansyah ◽  
Arief Setyanto ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Confusion Matrix ◽  
Sampling Technique ◽  
Host Population ◽  
Support Vector ◽  
Data Sets ◽  
Test Results ◽  
Data Set ◽  
Tourist Attractions ◽  
Svm Algorithm

Traveling activities are increasingly being carried out by people in the world. Some tourist attractions are difficult to reach hotels because some tourist attractions are far from the city center, Airbnb is a platform that provides home or apartment-based rentals. In lodging offers, there are two types of hosts, namely non-super host and super host. The super-host badge is obtained if the innkeeper has a good reputation and meets the requirements. There are advantages to being a super host such as having more visibility, increased earning potential and exclusive rewards. Support Vector Machine (SVM) algorithm classification process by these criteria data. Data set is unbalanced. The super host population is smaller than the non-super host. Overcoming the imbalance, this over sampling technique is carried out using ADASYN and SMOTE. Research goal was to decide the performance of ADASYN and sampling technique, SVM algorithm. Data analyses used over sampling which aims to handle unbalanced data sets, and confusion matrix used for testing Precision, Recall, and F1-SCORE, and Accuracy. Research shows that SMOTE SVM increases the accuracy rate by 1 percent from 80% to 81%, which is influenced by the increase in the True (minority) label test results and a decrease in the False label test results (majority), the SMOTE SVM is better than ADASYN SVM, and SVM without over sampling.

Fault Diagnosis Method Based on Supervised Manifold Learning and SVM

2011 ◽  
Vol 216 ◽  
pp. 223-227 ◽  
Author(s):  
Guang Bin Wang ◽  
Xue Jun Li ◽  
Zhi Cheng He ◽  
Y.Q. Kong
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Roller Bearing ◽  
Support Vector ◽  
Alignment Algorithm ◽  
Bearing Fault Diagnosis ◽  
Svm Algorithm ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Local Tangent

In order to better identify the fault of bearing,one new fualt diagnosis method based on supervised Linear local tangent space alignment (SLLTSA) and support vector machine (SVM) is proposed..In this methd, the supervised learning is embedded into the linear local tangent space alignment algorithm,making full use of experience category information for fault feature extraction, and then using linear transformation matrix to fast process the new monitoring data, finally distinguishing fault status of the incremental data by nonlinear SVM algorithm. The experiment result for roller bearing fault diagnosis shows that SLLTSA-SVM method has better diagnosis effect than related unsupervised methods.

Hybrid Algorithm for Deforestation Detection Using Satellite Data by Using Support Vector Machine (SVM) Algorithm

2018 ◽  
Vol 6 (6) ◽  
pp. 92 ◽  
Author(s):  
J Suresh Babu ◽  
T. Sudha
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Training Data ◽  
Geographical Information ◽  
Support Vector ◽  
Data Sets ◽  
Forest Cover Change ◽  
Svm Algorithm ◽  
Vector Machines

There are many applications of Remote sensing Satellite images like (astronomy, military, forecasting, and geographical information). Using satellite remote sensing data sets we developed the mapping forest area cover change. This kind of multiple improvement and identifying methods have a Training Data Automation algorithm which is used for advanced vector machines procedure. This TDM technique capable of automatically generating exact image enhanced patches. The obtained high resolute training data allow in producing the dependable forest cover change products with the help of SVM. This process was tested in study areas selected from major forest areas across the globe. In each area, a forest cover change map was produced using a pair of real time Land sat images acquired around 1999 and 2015.

Railway Turnout Fault Diagnosis Based on Support Vector Machine

2014 ◽  
Vol 556-562 ◽  
pp. 2663-2667 ◽  
Author(s):  
You Min He ◽  
Hui Bing Zhao ◽  
Jian Tian ◽  
Meng Qi Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Grid Search ◽  
Model Based ◽  
Proper Method ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Railway Turnout

The maintenance efficiency of Chinese railway turnout is closely related to the accuracy of its fault diagnosis method. A proper method will provide great help to railway staff in maintaining turnouts. The research introduced in this paper built a model based on Support Vector Machine (SVM) and Grid Search and later than tested its effect with the data from experiments. Result of that test shows that the method can achieve a diagnosis accuracy as high as 98.33%.

scholarly journals Machine Learning Applications based on SVM Classification A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 81-90
Author(s):  
Dakhaz Mustafa Abdullah ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Learning Techniques ◽  
Svm Algorithm ◽  
Machine Learning Applications

Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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