Fault diagnosis of high-speed railway turnout based on support vector machine

2016 ◽  
Author(s):  
Fenfang Zhou ◽  
Li Xia ◽  
Wei Dong ◽  
Xinya Sun ◽  
Xiang Yan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
High Speed ◽  
Support Vector ◽  
High Speed Railway ◽  
Railway Turnout

Short-Time Fluctuation Characteristic and Combined Forecasting of High-Speed Railway Passenger Flow Based on EEMD

2013 ◽  
Vol 409-410 ◽  
pp. 1071-1074
Author(s):  
Xiu Shan Jiang ◽  
Rui Feng Zhang ◽  
Liang Pan
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Mode Decomposition ◽  
Railway Passenger ◽  
Short Term Forecasting

Take Wuhan-Guangzhou high-speed railway for example. By adopting the empirical mode decomposition (EMD) attempt to analyze mode from the perspective of volatility of high speed railway passenger flow fluctuation signal. Constructed the ensemble empirical mode decomposition-gray support vector machine (EEMD-GSVM) short-term forecasting model which fuse the gray generation and support vector machine with the ensemble empirical mode decomposition (EEMD). Finally, by the accuracy of predicted results, explains the EEMD-GSVM model has the better adaptability.

scholarly journals Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 163
Author(s):  
Ling-Ling Li ◽  
Jia-Qi Liu ◽  
Wei-Bing Zhao ◽  
Lei Dong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
High Speed ◽  
Dc Motor ◽  
Support Vector ◽  
Brushless Dc Motor ◽  
Gray Wolf ◽  
Brushless Dc ◽  
Permanent Magnet Brushless Dc

With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.

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 Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Yulin Zhu ◽  
Youpeng Zhang ◽  
Zhongji Su
Keyword(s):  
Natural Language Processing ◽  
Fault Diagnosis ◽  
Natural Language ◽  
Language Processing ◽  
High Speed ◽  
Topic Model ◽  
Western China ◽  
Support Vector ◽  
High Speed Railway ◽  
The Impact

The Lanzhou-Xinjiang (Lan-Xin) high-speed railway is one of the principal sections of the railway network in western China, and signal equipment is of great importance in ensuring the safe and efficient operation of the high-speed railway. Over a long period, in the railway operation and maintenance process, the railway signaling and communications department has recorded a large amount of unstructured text information about equipment faults in the form of natural language. However, due to irregularities in the recording methods of these data, it is difficult to use directly. In this paper, a method based on natural language processing (NLP) was adopted to analyze and classify this information. First, the Latent Dirichlet Allocation (LDA) topic model was used to extract the semantic features of the text, which were then expressed in the corresponding topic feature space. Next, the Support Vector Machine (SVM) algorithm was used to construct a signal equipment fault diagnostic model that reduced the impact of sample data imbalance on the classification accuracy. This was compared and analyzed with the traditional Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms. This study used signal equipment failure text data from the Lan-Xin high-speed railway to conduct experimental analysis and verify the effectiveness of the proposed method. Experiments showed that the accuracy of the SVM classification algorithm could reach 0.84 after being combined with the LDA topic model, which verifies that the natural language processing method can effectively realize the fault diagnosis of signal equipment and has certain guiding significance for the maintenance of field signal equipment.

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