Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Mingliang Cui ◽  
Youqing Wang ◽  
Xinshuang Lin ◽  
Maiying Zhong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Rolling Bearings

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.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

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