scholarly journals Experimental investigation and multi-conditions identification method of centrifugal pump using Fisher discriminant ratio and support vector machine

2019 ◽  
Vol 11 (9) ◽  
pp. 168781401987804
Author(s):  
Guangqi Qiu ◽  
Si Huang ◽  
Yingkui Gu
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Domain Analysis ◽  
Test System ◽  
Support Vector ◽  
Centrifugal Pumps ◽  
Flow Conditions ◽  
Identification Method ◽  
Fisher Discriminant ◽  
Time Flow

For identifying the operation situations of centrifugal pumps by artificial intelligence, we performed an experiment on multi-flow conditions. The multi-flow conditions were simulated by adjusting an automatic flow-regulating valve installed on outlet pipe, and the vertical vibration signals of 20 flow points at the bearing house were collected by the test system. By time-domain analysis, frequency-domain analysis, information entropy, empirical modal decomposition, and wavelet packet decomposition methods, a comprehensive feature space was constructed. In addition, the optimal features were selected by Fisher discriminant ratio, and the dimensionality of the selected optimal features was reduced with principal component analysis. Finally, support vector machine algorithm was employed to identify the real-time flow condition, and the hyper-parameters of support vector machine classifier model were optimized by a grid search technique. Results show that the vibration test can effectively simulate the operation situation of centrifugal pumps under multi-flow conditions, and the proposed multi-flow conditions identification method has achieved a good identification performance.

An Identification Method for Cracked Eggs Based on Image Wavelet Transform and Multi-Features Synthesis

2014 ◽  
Vol 704 ◽  
pp. 412-418
Author(s):  
Li Rong Xiong ◽  
Zhi Hui Zhu
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
High Frequency ◽  
Vision System ◽  
Joint Probability ◽  
Principal Component ◽  
Ccd Camera ◽  
Classification Model ◽  
Support Vector ◽  
Identification Method

An identification method for cracked eggs by means of the digital image technology was proposed in this paper. Firstly, an ideal machine vision system was built and the images of good eggs and cracked eggs were obtained by CCD camera. Secondly, each image was decomposed on two layers of wavelet, so 6 high-frequency sub-images and 2 low-frequency sub-images were extracted. Then joint probability matrix after wavelet transform had been calculated and 5 parameters for each high-frequency sub-images were extracted, so the total of the joint probability matrix parameters was 30 for 6 high-frequency sub-images. At the same time, 10 wavelet energy parameters were obtained. Thirdly, four main factor component scores were selected from above 40 feature parameters after principal component analysis, which were input to support vector machine. Finally, classification model was built by support vector machine. Experiments show that the proposed method was effective to identify the cracked eggs from good eggs and the identification rate was 93.75%.

scholarly journals Structural Damage Identification Based on the Transmissibility Function and Support Vector Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yansong Diao ◽  
Xue Men ◽  
Zuofeng Sun ◽  
Kongzheng Guo ◽  
Yumei Wang
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Damage Identification ◽  
Wavelet Packet ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Detection Accuracy ◽  
Wavelet Packet Decomposition ◽  
Identification Method

A novel damage identification method based on transmissibility function and support vector machine is proposed and outlined in this paper. Basically, the transmissibility function is calculated with the acceleration responses from damaged structure. Then two damage features, namely, wavelet packet energy vector and the low order principal components, are constructed by analyzing the amplitude of the transmissibility function with wavelet packet decomposition and principal component analysis separately. Finally, the classification algorithm and regression algorithm of support vector machine are employed to identify the damage location and damage severity respectively. The numerical simulation and shaking table model test of an offshore platform under white noise excitation are conducted to verify the proposed damage identification method. The results show that the proposed method does not need the information of excitation and the data from undamaged structure, needs only small size samples, and has certain antinoise ability. The detection accuracy of the proposed method with damage feature constructed by principal component analysis is superior to that constructed by wavelet packet decomposition.

An Accurate Packer Identification Method Using Support Vector Machine

2014 ◽  
Vol E97.A (1) ◽  
pp. 253-263 ◽  
Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Identification Method

scholarly journals Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianwei Cui ◽  
Mengxiao Shan ◽  
Ruqiang Yan ◽  
Yahui Wu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Energy Difference ◽  
Test System ◽  
Support Vector ◽  
Svm Classifier ◽  
Aero Engine ◽  
Aero Engines ◽  
Optimal Set

This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

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