scholarly journals Application of Projection Pursuit Analysis Method Based on Kernel Function in Fault Diagnosis for Rolling Bearing

2021 ◽  
Vol 770 (1) ◽  
pp. 012001
Author(s):  
Cheng Jing ◽  
Su Le ◽  
Wang Weiqing ◽  
He Shan
Keyword(s):  
Fault Diagnosis ◽  
Kernel Function ◽  
Projection Pursuit ◽  
Rolling Bearing ◽  
Analysis Method

A Rolling Bear Fault Diagnosis Method Based on Improved LMD and Order Analysis

2014 ◽  
Vol 530-531 ◽  
pp. 256-260
Author(s):  
Hui Juan Yuan ◽  
Jia Qi ◽  
Hong Mei Li ◽  
Jun Zhong Li ◽  
Xue Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Frequency Modulation ◽  
Rolling Bearing ◽  
Analysis Method ◽  
Vibration Signals ◽  
Order Analysis ◽  
Diagnosis Method ◽  
Bearing Vibration

This document explains and demonstrates how to predict the fault point of rolling bear. Rolling bearing vibration signals are decomposed by the LMD method to get several single components including amplitude modulation and frequency modulation signals. Combing the order analysis method can get the fault point of rolling bear.

scholarly journals A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

2019 ◽  
Vol 9 (11) ◽  
pp. 2356 ◽  
Author(s):  
Yinsheng Chen ◽  
Tinghao Zhang ◽  
Zhongming Luo ◽  
Kun Sun
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Sample Entropy ◽  
Analysis Method ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy ◽  
Fault Severity ◽  
Fault Features

To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.

Fault Diagnosis of Rolling Bearing Based on the PSO-SVM of the Mixed-Feature

2013 ◽  
Vol 380-384 ◽  
pp. 895-901 ◽  
Author(s):  
Jun Ma ◽  
Jian De Wu ◽  
Yu Gang Fan ◽  
Xiao Dong Wang ◽  
Zong Kai Shao
Keyword(s):  
Fault Diagnosis ◽  
Kernel Function ◽  
Optimal Parameter ◽  
Monitoring Program ◽  
Rolling Bearing ◽  
Pso Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Swarm Optimization ◽  
Diagnosis Method

The rolling bearing is one of the most important and widely used parts in the rotating machinery. It is necessary to establish a reliable condition monitoring program which can avoid serious fault in the runtime and diagnose failure timely and accurately when it happens. This paper puts forward to a fault diagnosis method of rolling bearing based on the PSO-SVM of the mixed-feature. Firstly, we extract features in time domain, frequency domain, and order quenfrency domain. Secondly, select both Support Vector Machine (SVM) parameters by Particle Swarm Optimization (PSO) algorithm and kernel function of SVM classification model. Finally, classification model of SVM is designed by using the extracted salient features, kernel function and optimal parameter of PSO. The result verifies the effectiveness of the proposed method.

Adaptive radial sinc kernel distribution and its application in mechanical fault diagnosis

2016 ◽  
Vol 231 (3) ◽  
pp. 485-493 ◽  
Author(s):  
Zhinong Li ◽  
Ming Zhu ◽  
Fulei Chu ◽  
Xuping He
Keyword(s):  
Fault Diagnosis ◽  
Frequency Distribution ◽  
Kernel Function ◽  
Rolling Bearing ◽  
Kernel Functions ◽  
Frequency Resolution ◽  
Good Time ◽  
Time Frequency ◽  
Fixed Kernel ◽  
Time Frequency Distribution

Based on the deficiency of fixed-kernel in the traditional time–frequency distribution, which is lack of adaptability, a new adaptive kernel function, which is named as the adaptive radial sinc kernel, is proposed according to design criteria of adaptive optimal kernel. The definition and algorithm of radial sinc kernel are given, and the proposed method is compared with the tradition time–frequency distribution. The simulation results show that the proposed method is superior to the traditional fixed-kernel functions, such as Wigner–Ville distribution, Choi–Williams distribution, cone-kernel distribution and continuous wavelet transform. The adaptive radial sinc kernel can overcome the deficiency of fixed-kernel function in traditional time–frequency distribution, adopt the optimizing method to filter the cross-terms adaptively according to the signal distribution, obtain good time–frequency resolution and has extensive adaptability for an arbitrary signal. Finally, the proposed method has been applied to the fault diagnosis of rolling bearing, and the experiment result shows that the proposed method is very effective.

Fault Diagnosis of Rolling Bearing Based on Kernel Independent Component Analysis by Using Mixed Kernel Function

2013 ◽  
Vol 312 ◽  
pp. 593-596 ◽  
Author(s):  
Bo Zeng ◽  
An Hua Chen ◽  
Ling Li Jiang
Keyword(s):  
Fault Diagnosis ◽  
Kernel Function ◽  
Kernel Method ◽  
Rolling Bearing ◽  
Independent Component ◽  
Fusion Method ◽  
Bearing Fault Diagnosis ◽  
Weighted Fusion ◽  
Kernel Independent Component Analysis ◽  
Kernel Parameters

Studies have shown that the type of kernel function and parameters have a very important impact on the performance of the kernel method. Aiming at the requirement of rolling bearing fault diagnosis, this paper presents a mixed kernel function of kernel independent component and studies on the optimization of its kernel parameters. The mixed kernel function is constructed based on the weighted fusion method, and the kernel parameters are optimized by using the genetic algorithm. The improved kernel independent component method is used for fault diagnosis of rolling bearing, and the testing results demonstrate that it is an effective method.

scholarly journals A rolling bearing fault diagnosis method based on LSSVM

2020 ◽  
Vol 12 (1) ◽  
pp. 168781401989956
Author(s):  
Xuejin Gao ◽  
Hongfei Wei ◽  
Tianyao Li ◽  
Guanglu Yang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Kernel Function ◽  
Dimensional Space ◽  
Rolling Bearing ◽  
Classification Model ◽  
Support Vector ◽  
Bearing Fault ◽  
Diagnosis Method

The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

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