Fault Diagnosis of Rolling Bearings Based on IMF Envelope Sample Entropy and Support Vector Machine

2013 ◽  
Vol 10 (16) ◽  
pp. 5189-5198 ◽  
Author(s):  
Keheng Zhu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Sample Entropy ◽  
Support Vector ◽  
Rolling Bearings

scholarly journals Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Shuang Pan ◽  
Tian Han ◽  
Andy C. C. Tan ◽  
Tian Ran Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Vibration Signals ◽  
Vibration Data ◽  
Motor Vibration

An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.

scholarly journals Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5297
Author(s):  
Jie Lv ◽  
Wenlei Sun ◽  
Hongwei Wang ◽  
Fan Zhang
Keyword(s):  
Fault Diagnosis ◽  
Search Algorithm ◽  
Decomposition Methods ◽  
Sample Entropy ◽  
Signal Decomposition ◽  
Support Vector ◽  
Optimal Parameters ◽  
Rolling Bearings ◽  
Signal Features ◽  
Mode Decomposition

We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

scholarly journals Rolling Bearings Fault Diagnosis under Variable Conditions Using RCMFE and Improved Support Vector Machine

2020 ◽  
Vol 25 (3) ◽  
pp. 304-317
Author(s):  
Xin Zhang ◽  
Jian-min Zhao ◽  
Hai-ping Li ◽  
Rui-feng Yang ◽  
Hong-zhi Teng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
High Efficiency ◽  
Vibration Signal ◽  
Support Vector ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Whale Optimization ◽  
Improved Support Vector Machine

As critical components, rolling bearings are widely used in a variety of rotating machinery. It is necessary to develop a suitable fault diagnosis method to prevent malfunctions and breakages of bearings during operation. However, the current methods for the fault diagnosis of rolling bearings are too cumbersome to be applied in practical engineering. In addition, the working condition of rolling bearings is generally tough, complex, and especially variable. These conditions cause fault diagnosis methods to be less effective. This paper aims to provide a simple and effective method for the fault diagnosis of rolling bearings under variable conditions. The main contribution of this paper is as follows: (1) The refined composite multiscale fuzzy entropy (RCMFE) is applied in bearing fault feature extraction because of its simplicity and high efficiency; (2) The improved support vector machine (ISVM), based on the whale optimization algorithm (WOA), is proposed to identify the fault pattern of rolling bearings. The ISVM is proposed in this paper to solve the problem that parameter setting affects the classification effect of SVM. In the ISVM, the WOA is employed to optimize both the regularization and kernel parameters of the SVM. Compared with the traditional optimization methods, the WOA has the advantages of high optimization speed and better optimization ability; (3) Combining the RCMFE and the ISVM to diagnose bearing fault under variable working conditions. The effectiveness of the RCMFE-ISVM has been validated via experimental vibration signal of bearings faults under variable working conditions.

scholarly journals Fault diagnosis method of rolling bearings Based on SPA-FE-IFSVM

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096947
Author(s):  
Hui Han ◽  
Lina Hao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Rolling Bearings ◽  
Data Set ◽  
Diagnosis Method

Rolling bearings are the most frequently failed components in rotating machinery. Once a failure occurs, the entire system will be shut down or even cause catastrophic consequences. Therefore, a fault detection of rolling bearings is of great significance. Due to the complexity of the mechanical system, the randomness of the vibration signal appears on different scales. Based on the multi-scale fuzzy entropy (FE) analysis of the vibration signal, a rolling bearing fault diagnosis method based on smoothness priors approach (SPA) -FE-IFSVM is proposed. The SPA method was used to adaptively decompose the vibration signal and obtain the trend item and de-trend item of the vibration signal. Then the fuzzy entropy of the trend item and de-trend item was calculated respectively. Meanwhile, aiming at the problem that the support vector machine (SVM) cannot process the data set containing fuzzy messages and was sensitive to noise, the fuzzy support vector machine (FSVM) was introduced and improved, and then the FE as the feature vector was input into the improved fuzzy support vector machine (IFSVM) to identify the failure. The method was applied to the rolling bearing experimental data. The analysis results show that: this method can achieve 100% fault diagnosis accuracy when only two component features are extracted, which can effectively realize the fault diagnosis of rolling bearings.

A Practical Method for Multi-Fault Diagnosis of Rolling Element Bearings Using Discrete Wavelet Transform and Support Vector Machine

2006 ◽  
Author(s):  
Saeed Abbasion ◽  
Anoushiravan Farshidianfar ◽  
Nilgoon Irani ◽  
Mohamad Bashari
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Practical Method ◽  
Rolling Bearing ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Good Resolution ◽  
Rolling Bearings ◽  
Time Frequency

Due to importance of rolling bearings as one of the most widely used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to prevent malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method have good computational efficiency, and have good resolution in both, time and frequency domain. The point of interest in this investigation is the present of an effective method for multi fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis and support vector machine (SVM). The system that is under study is an electric motor which has two rolling bearings, one of them is next to the output shaft and the other one is next to the fan and for each of them there is one normal form and three false forms, which make 8 forms for study. The outcome that we have achieved from wavelet analysis and SVM are fully in agreement with empirical result.

Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM

2011 ◽  
Vol 199-200 ◽  
pp. 927-930
Author(s):  
Zi Fa Li ◽  
Jin Guo Li
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Rolling Bearings ◽  
Data Set ◽  
Vibration Signals ◽  
Principle Components Analysis ◽  
Components Analysis ◽  
Pure Quaternion

A new method had been proposed in this paper of fault diagnosis for rolling bearings based on multichannel vibration signals and QPCA-SVM-based method. The vibration signals were obtained by some multi-sensors with three channels X, Y, Z, that were orthogonal axes. The three orthogonal axes signals were constructed a pure quaternion sequences as samples for processing. The pure quaternion sequences data set was processed by quaternion principle components analysis (QPCA) for feature extraction, and then combined with pattern recognition tools support vector machine (SVM) for classifying some faults patterns. The experimental results indicated its efficiency, and it provided a method for fault diagnosis on multichannel vibration signals.

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