Intelligent Fault Diagnosis Method Based on Vector-Bispectrum and SVDD

2012 ◽  
Vol 490-495 ◽  
pp. 1029-1033 ◽  
Author(s):  
Ling Jun Li ◽  
Wen Ping Lei ◽  
Jie Han ◽  
Wang Shen Hao
Keyword(s):  
Fault Diagnosis ◽  
Field Vector ◽  
Classification Model ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Nonlinear Properties ◽  
Channel Information ◽  
Diagnosis Method ◽  
Set Up

Support vector data description (SVDD) can be used to solve the problems of the insufficient fault samples in the fault diagnosis field. Vector-bispectrum is the bispectrum analysis method based on the full vector spectrum information fusion. It can be used to fuse the double-channel information of the rotary machines effectively and reflect the nonlinear properties in the signals more completely and accurately. In order to realize the aim that the faults of the machines can be diagnosed effectually and intelligently under the situation of the lack of the fault samples, the intelligent diagnosis method of the faults by combining the vector-bispectrum with SVDD is put forward. By using the vector-bispectrum to process the signals and extract the characteristic vectors, which can be used as the input parameters of SVDD. The classification model is set up and therefore the running states of the machines can also be classified. The method is applied to the gearbox fault diagnosis. The results indicate that the method can be effectively used to extract the characteristic information of the gearbox signals and increase the accuracy of SVDD in the fault diagnosis.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

scholarly journals Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

2019 ◽  
Vol 9 (8) ◽  
pp. 1676 ◽  
Author(s):  
Tan ◽  
Fu ◽  
Wang ◽  
Xue ◽  
Hu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Rolling Bearing ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Decision Strategy ◽  
Vector Data ◽  
Data Description ◽  
Adaptive Parameter ◽  
Diagnosis Method

Rolling bearing is of great importance in modern industrial products, the failure of which may result in accidents and economic losses. Therefore, fault diagnosis of rolling bearing is significant and necessary and can enhance the reliability and efficiency of mechanical systems. Therefore, a novel fault diagnosis method for rolling bearing based on semi-supervised clustering and support vector data description (SVDD) with adaptive parameter optimization and improved decision strategy is proposed in this study. First, variational mode decomposition (VMD) was applied to decompose the vibration signals into sets of intrinsic mode functions (IMFs), where the decomposing mode number K was determined by the central frequency observation method. Next, fuzzy entropy (FuzzyEn) values of all IMFs were calculated to construct the feature vectors of different types of faults. Later, training samples were clustered with semi-supervised fuzzy C-means clustering (SSFCM) for fully exploiting the information inside samples, whereupon a small number of labeled samples were able to provide sufficient data distribution information for subsequent SVDD algorithms and improve its recognition ability. Afterwards, SVDD with improved decision strategy (ID-SVDD) that combined with k-nearest neighbor was proposed to establish diagnostic model. Simultaneously, the optimal parameters C and σ for ID-SVDD were searched by the newly proposed sine cosine algorithm improved with adaptive updating strategy (ASCA). Finally, the proposed diagnosis method was applied for engineering application as well as contrastive analysis. The obtained results reveal that the proposed method exhibits the best performance in all evaluation metrics and has advantages over other comparison methods in both precision and stability.

Adjustment mode decision based on support vector data description and evidence theory for assembly lines

2018 ◽  
Vol 118 (8) ◽  
pp. 1711-1726 ◽  
Author(s):  
Youlong Lv ◽  
Wei Qin ◽  
Jungang Yang ◽  
Jie Zhang
Keyword(s):  
Assembly Line ◽  
Historical Data ◽  
Classification Model ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Assembly Lines ◽  
Content Type ◽  
Input Layer ◽  
Multi Classification

PurposeThree adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.Design/methodology/approachThe decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.FindingsThe proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.Practical implicationsThis research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.Originality/valueThe adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

scholarly journals Fault Diagnosis in Condition of Sample Type Incompleteness Using Support Vector Data Description

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Hui Yi ◽  
Zehui Mao ◽  
Bin Jiang ◽  
Cuimei Bo ◽  
Yufang Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Training Sample ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Sample Type ◽  
Vector Data ◽  
Data Description ◽  
Incomplete Sample ◽  
The Relationship

Faulty samples are much harder to acquire than normal samples, especially in complicated systems. This leads to incompleteness for training sample types and furthermore a decrease of diagnostic accuracy. In this paper, the relationship between sample-type incompleteness and the classifier-based diagnostic accuracy is discussed first. Then, a support vector data description-based approach, which has taken the effects of sample-type incompleteness into consideration, is proposed to refine the construction of fault regions and increase the diagnostic accuracy for the condition of incomplete sample types. The effectiveness of the proposed method was validated on both a Gaussian distributed dataset and a practical dataset. Satisfactory results have been obtained.

Fault diagnosis of rolling element bearing compound faults based on sparse no-negative matrix factorization-support vector data description

2016 ◽  
Vol 24 (2) ◽  
pp. 272-282 ◽  
Author(s):  
Hongchao Wang
Keyword(s):  
Fault Diagnosis ◽  
Matrix Factorization ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Outer Race ◽  
Rolling Element ◽  
Sparse Coefficient ◽  
Element Bearing

The bispectrum of rolling element bearing compound faults contains abundant fault characteristic information, and how to extract the fault feature effectively is a key problem. The fault diagnosis method of rolling element bearing compound faults based on Sparse No-Negative Matrix Factorization (SNMF)-Support Vector Data Description (SVDD) is proposed in the paper. The figure handling method SNMF is used firstly in fault feature extraction of the bispectrums of rolling element bearing different kinds of compound faults and the sparse coefficient matrices of the bispectrums are obtained. The sparse coefficient matrices are used as training and test input vectors of SVDD. At last, the three kinds of rolling element bearing compound faults (inner race outer race compound faults, outer race rolling element compound faults and inner race outer race rolling element compound faults) are classified correctly. In order to verify the advantages of the proposed method, the diagnosis results of the same three kinds of rolling element bearing compound faults based on No-Negative Matrix Factorization (NMF)-SVDD is used as comparison. The proposed method provides a new idea for fault diagnosis of rolling element bearing compound faults.

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