Application of Multi-Sensor Information Fusion Method Based on Rough Sets and Support Vector Machine

2016 ◽  
Author(s):  
Jinxue XUE ◽  
Guohu WANG ◽  
Xiaoqiang WANG ◽  
Fengkui CUI
Keyword(s):  
Support Vector Machine ◽  
Information Fusion ◽  
Rough Sets ◽  
Support Vector ◽  
Fusion Method ◽  
Sensor Information

scholarly journals Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System

2019 ◽  
Vol 9 (2) ◽  
pp. 300
Author(s):  
Jianyong Zuo ◽  
Jingxian Ding ◽  
Furen Feng
Keyword(s):  
Support Vector Machine ◽  
Information Fusion ◽  
Small Sample ◽  
Support Vector ◽  
Ratio Test ◽  
Fusion Method ◽  
Source Information ◽  
Braking System ◽  
Chinese Standard ◽  
Multiple Units

To identify and diagnose the latent leakage faults of key pneumatic units in the Chinese standard Electric Multiple Units (EMU) braking system, a multi-source information fusion method based on Kalman filtering, sequential probability ratio test (SPRT), and support vector machine (SVM) is proposed. The relay valve is taken as an example for research. Firstly, Kalman's state estimation function is used to obtain the innovation sequence, and the innovation sequence is input into the SPRT model to help recognize latent leakage faults of the relay valve. Using this method, the problem of the incomplete training set of the traditional SPRT method due to the change of the braking level and the vehicle load is solved. Secondly, the eight time-domain parameters of the relay valve input and the output pressure signal are extracted as fault characteristics, and then input to the support vector machine to realize the internal and external leakage fault diagnosis of the relay valve, which provides a reference for maintenance. Finally, this method is verified by the fault simulation data by quickly identifying latent leakage faults and diagnosing the internal and external leakage at a fault recognition rate of 100% by SVM under small sample conditions.

Bearing Fault Diagnosis Based on Multi-Sensor Information Fusion with SVM

2010 ◽  
Vol 34-35 ◽  
pp. 995-999 ◽  
Author(s):  
Xue Jun Li ◽  
D.L. Yang ◽  
Ling Li Jiang
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Binary Tree ◽  
Recognition Rate ◽  
Support Vector ◽  
Bearing Fault ◽  
Penalty Factor ◽  
Sensor Information

This paper proposed a fault diagnosis based on multi-sensor information fusion for rolling bearing. This method used the energy value of multiple sensors is used as feature vector and a binary tree support vector machine (Binary Tree Support Vector Machine, BT-SVM) is used for pattern recognition and fault diagnosis. By analyzing the training samples, penalty factor and the kernel function parameters have effects on the recognition rate of bearing fault, then a approximate method to determine optimum value are proposed, Compared with the traditional single sensor by using the components energy of EMD as feature, the results show that the proposed method in this paper significantly reduce feature extraction time, and improve diagnostic accuracy, which is up to99.82%. This method is simple, effective and fast in feature extraction and meets the bearing diagnosis requirement of real-time fault diagnosis.

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