Fault diagnosis of rolling element bearings based on complexity measure and ν support vector machine

2013 ◽  
Vol 55 (3) ◽  
pp. 142-146 ◽  
Author(s):  
Guofeng Wang ◽  
Chang Liu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Complexity Measure ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Rolling Element

scholarly journals Application of Feature Fusion Using Coaxial Vibration Signal for Diagnosis of Rolling Element Bearings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Jiao ◽  
Jianhai Yue ◽  
Di Pei ◽  
Zhunqing Hu
Keyword(s):  
Fault Diagnosis ◽  
Feature Fusion ◽  
Random Noise ◽  
Vibration Signal ◽  
Fault Classification ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Rolling Element ◽  
Vibration Signal Analysis

The research of rolling element bearings (REBs) fault diagnosis based on single sensor vibration signal analysis is very common. However, the information provided by an individual sensor is very limited, and the robustness of the system is poor. In this paper, a novel fault diagnosis method based on coaxial vibration signal feature fusion (CVSFF) is proposed to fully analyze the multisensor information of the system and build a more reliable diagnostic system. An ensemble empirical mode decomposition (EEMD) method is used to decompose the original vibration signal into a number of intrinsic mode functions (IMFs). Then the autocorrelation analysis is introduced to reduce the random noise remaining in IMFs. After that, the Rényi entropy is calculated as the feature of bearings. Finally, the features of coaxial vibration signal are fused by a multiple-kernel learning support vector machine (MKL-SVM) to classify bearing conditions. In order to verify the effectiveness of the CVSFF method in REB diagnosis, eight data sets from the Case Western Reserve University Bearing Data Center are selected. The fault classification results demonstrate that the proposed approach is a valuable tool for bearing faults detection, and the fused feature from coaxial sensors improves fault classification accuracy for REBs.

Fault Diagnosis of Metallurgical Machinery Based on Spectral Kurtosis and GA-SVM

2013 ◽  
Vol 634-638 ◽  
pp. 3958-3961 ◽  
Author(s):  
Yun Li ◽  
Yan Gao ◽  
Jun Guo ◽  
Xian Jun Yu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Simulation Study ◽  
High Accuracy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Spectral Kurtosis ◽  
Rolling Element ◽  
Element Bearing

This paper proposed a new method of rolling element bearing (REB) fault diagnosis for metallurgical machinery. Mainly it stresses on the combination of spectral kurtosis (SK) and supports vector machine (SVM), using genetic algorithm (GA) to optimize the parameters of support vector machine at the same time. Thus, this study aims to integrate SK, GA and SVM in order to develop an intelligent REB fault detector for metallurgical machineries. Simulation study indicates that this method can effectively detect the REB faults with a high accuracy.

A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine

2015 ◽  
Vol 230 (13) ◽  
pp. 2314-2322 ◽  
Author(s):  
Keheng Zhu ◽  
Haolin Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

Aiming at the non-linear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a new rolling element bearing fault diagnosis method based on hierarchical fuzzy entropy and support vector machine is proposed in this paper. By incorporating the advantages of both the concept of fuzzy sets and the hierarchical decomposition of hierarchical entropy, hierarchical fuzzy entropy is developed to extract the fault features from the bearing vibration signals, which can provide more useful information reflecting bearing working conditions than hierarchical entropy. After feature extraction with hierarchical fuzzy entropy, a multi-class support vector machine is trained and then employed to fulfill an automated bearing fault diagnosis. The experimental results demonstrate that the proposed approach can identify different bearing fault types as well as severities precisely.

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