Fault Diagnosis of Conveyance Machine Based on Fuzzy Support Vector

2011 ◽  
Vol 135-136 ◽  
pp. 547-552
Author(s):  
Yuan Bin Hou ◽  
Ning Li ◽  
Fan Guo ◽  
Jing Chen
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Kernel Function ◽  
Coal Mine ◽  
Fuzzy Theory ◽  
Kernel Functions ◽  
Mine Safety ◽  
Support Vector ◽  
Coal Mine Safety ◽  
Rubber Belt

Aiming random and nonlinearity for conveyance machine of rubber belt in mine, a method of fault diagnosis is presented which fusion of fuzzy theory and support vector machine (FSVM). According to the coal mine safety rules of the regulation, the conveyance machine servicing are deduced eleven faults after analyzing practice statistic data, here, we consider some are fuzzy that the statistic data are divided to the normal kind or fault kind, but some are definite that the statistic data possibility are belong to same kind fault, accordingly, the fuzzy support vectors is established. Farther, two kernel functions of FSVM is made for seeking the problem of random and nonlinearity, which are RBF and TANH. According to the random statistic data and the study sample, analyzing the effect of expense and kernel function in selecting different parameters, the unitary constant is ascertained, next, the FSVM kernel function of fault diagnosis multi-class rules are established, then, this method availability is proved using test data and simulation.

scholarly journals Coal Mine Gas Safety Evaluation Based on Adaptive Weighted Least Squares Support Vector Machine and Improved Dempster–Shafer Evidence Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhenming Sun ◽  
Dong Li
Keyword(s):  
Coal Mine ◽  
Evaluation Model ◽  
Weighted Least Squares ◽  
Evidence Theory ◽  
Safety Evaluation ◽  
Mine Safety ◽  
Sensor Data ◽  
Support Vector ◽  
Coal Mine Safety ◽  
High Conflict

Gas safety evaluation has always been vital for coal mine safety management. To enhance the accuracy of coal mine gas safety evaluation results, a new gas safety evaluation model is proposed based on the adaptive weighted least squares support vector machine (AWLS-SVM) and improved Dempster–Shafer (D-S) evidence theory. The AWLS-SVM is used to calculate the sensor value at the evaluation time, and the D-S evidence theory is used to evaluate the safety status. First, the sensor data of gas concentration, wind speed, dust, and temperature were obtained from the coal mine safety monitoring system, and the prediction results of sensor data are obtained using the AWLS-SVM; hence, the prediction results would be the input of the evaluation model. Second, because the basic probability assignment (BPA) function is the basis of D-S evidence theory calculation, the BPA function of each sensor is determined using the posterior probability modeling method, and the similarity is introduced for optimization. Then, regarding the problem of fusion failure in D-S evidence theory when fusing high-conflict evidence, using the idea of assigning weights, the importance of each evidence is allocated to weaken the effect of conflicting evidence on the evaluation results. To prevent the loss of the effective information of the original evidence followed by modifying the evidence source, a conflict allocation coefficient is introduced based on fusion rules. Ultimately, taking Qing Gang Ping coal mine located in Shaanxi province as the study area, a gas safety evaluation example analysis is performed for the assessment model developed in this paper. The results indicate that the similarity measures can effectively eliminate high-conflict evidence sources. Moreover, the accuracy of D-S evidence theory based on enhanced fusion rules is improved compared to the D-S evidence theory in terms of the modified evidence sources and the original D-S evidence theory. Since more sensors are fused, the evaluation results have higher accuracy. Furthermore, the multisensor data evaluation results are enhanced compared to the single sensor evaluation outcomes.

Research on Fault Diagnosis of PCCP Based on Support Vector Machine

2010 ◽  
Vol 108-111 ◽  
pp. 409-414
Author(s):  
Chun Ting Yang ◽  
Yang Liu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Signal Detection ◽  
Acoustic Signal ◽  
Kernel Functions ◽  
Support Vector ◽  
Catastrophic Failure ◽  
Training Samples ◽  
The World ◽  
Good Classification

Recent years, many utilities have experienced catastrophic rupture of critical Prestressing Concrete Cylinder Pipe (PCCP) lines throughout the world. Much attention has been focused on reliably assessing the condition of PCCP mains. However, assessment of embedded prestressing wire is difficult. Continuous acoustic monitoring can provide a means of locating problematic pipe sections. In this paper the application of support vector machine (SVM) in acoustic signal detection is proposed. And the effect of this method is investigated. Some key parameters of SVM and kernel functions are surveyed. SVM methods are more effective, especially for the case of lack of training samples. The experiment shows that the SVM method has good classification ability and robust performances. The techniques can provide the opportunity to identify problematic pipe sections and repair the pipe prior to failure. Therefore it can help to prolong the life of a suspect pipeline while minimizing the potential for catastrophic failure.

Kernel Parameter Selection for SVM Classification

2011 ◽  
pp. 44-55
Author(s):  
Manju Bala ◽  
R. K. Agrawal
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Classification Accuracy ◽  
Parameter Selection ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classification ◽  
Kernel Parameter ◽  
Benchmark Datasets

The choice of kernel function and its parameter is very important for better performance of support vector machine. In this chapter, the authors proposed few new kernel functions which satisfy the Mercer’s conditions and a robust algorithm to automatically determine the suitable kernel function and its parameters based on AdaBoost to improve the performance of support vector machine. The performance of proposed algorithm is evaluated on several benchmark datasets from UCI repository. The experimental results for different datasets show that the Gaussian kernel is not always the best choice to achieve high generalization of support vector machine classifier. However, with the proper choice of kernel function and its parameters using proposed algorithm, it is possible to achieve maximum classification accuracy for all datasets.

scholarly journals New Mixed Kernel Functions of SVM Used in Pattern Recognition

2016 ◽  
Vol 16 (5) ◽  
pp. 5-14 ◽  
Author(s):  
Hao Huanrui
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Kernel Methods ◽  
Pattern Analysis ◽  
New Technology ◽  
Wavelet Function ◽  
Kernel Functions ◽  
Good Time ◽  
Polynomial Kernel ◽  
Support Vector

Abstract The pattern analysis technology based on kernel methods is a new technology, which combines good performance and strict theory. With support vector machine, pattern analysis is easy and fast. But the existing kernel function fits the requirement. In the paper, we explore the new mixed kernel functions which are mixed with Gaussian and Wavelet function, Gaussian and Polynomial kernel function. With the new mixed kernel functions, we check different parameters. The results shows that the new mixed kernel functions have good time efficiency and accuracy. In image recognition we used SVM with two mixed kernel functions, the mixed kernel function of Gaussian and Wavelet function are suitable for more states.

scholarly journals Estimation of Heart Rate from Vocal Frequency Based on Support Vector Machine

2016 ◽  
Vol 2 (1) ◽  
pp. 16 ◽  
Author(s):  
Motoki Sakai
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Kernel Function ◽  
Prediction Models ◽  
Kernel Functions ◽  
Inner Function ◽  
Support Vector ◽  
Hyperbolic Tangent ◽  
Hyperbolic Tangent Function ◽  
Tangent Function

Heart rate (HR) is one of the vital signs used to assess our physical condition; it would be beneficial if HR could easily be obtained without special medical instruments. In this study, a feature of vocal frequency was used to estimate HR, because it can easily be recorded with a common device such as a smartphone. Previous studies proposed that a support vector machine (SVM) that adopted the inner product as the kernel function was efficient for estimating HR to a certain extent. However, these studies did not present the effectiveness of other kernel functions, such as the hyperbolic tangent function. Therefore, this study identified a combination of kernel functions of the kernel ridge regression (KRR). In addition, features of vocal frequency to effectively estimate HR were investigated. To evaluate the effectiveness, experiments were conducted with two subjects. In the experiment, 60 sets of HRs and voice data were measured per subject. To identify the most effective kernel function, four kernel functions (the inner function, Gaussian function, polynomial function, and hyperbolic tangent function) were compared. Moreover, effective features of vocal frequency were selected with the sequential feature selection (SFS) method. As a consequence, the hyperbolic tangent function worked best, and high-frequency components of voice were efficient. However, results of this research indicated that effective vocal spectrum components to estimate HR differ depending on prediction models.

scholarly journals Gear fault diagnosis method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881103 ◽  
Author(s):  
Lizheng Pan ◽  
Dashuai Zhu ◽  
Shigang She ◽  
Aiguo Song ◽  
Xianchuan Shi ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Kernel Function ◽  
Wavelet Packet ◽  
Component Analysis ◽  
Independent Component ◽  
Support Vector ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Aiming at the problem of gear fault diagnosis, in order to effectively extract the features and improve the accuracy of gear fault diagnosis, the method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion is proposed in this research. The proposed wavelet-packet independent component analysis feature extraction method can effectively combine the advantages of wavelet packet and independent component analysis methods and acquire more comprehensive feature information. Besides, the proposed kernel-function-fusion support vector machine can well integrate the advantage characteristics of each kernel function. The energy features of wavelet packet coefficients are acquired with four-layer wavelet packet decomposition and then the extracted energy features are further optimized by the independent component analysis method. The kernel-function-fusion support vector machine method is adopted to realize the gear fault diagnosis. Two kernel function models with the best self-classification accuracy are employed to serve the gear fault diagnosis corporately. The test samples are primarily classified by the main kernel function model, and then some samples are selected to be reclassified with the other kernel function model. Finally, the two kernel function models cooperate to determine the type of test samples. The comparison investigations demonstrate that the proposed method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion achieves very high diagnosis accuracy.

The Research of Face Recognition Based on Kernel Function

2013 ◽  
Vol 321-324 ◽  
pp. 1181-1185
Author(s):  
Yong Jun Zhu ◽  
Wen Bo Liu ◽  
Feng Yu Jin ◽  
Yin Wu
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Feature Space ◽  
Kernel Functions ◽  
Weighting Coefficient ◽  
Support Vector ◽  
New Learning ◽  
Learning Machine ◽  
Matrix Base ◽  
Selection Of

The way of kernel function has been widely applied in machine learning field, such as artificial neural network and support vector machine, for avoiding dimensional disaster of feature space and improving performance of learning machine effectively. The selection of kernel function and construction of new kernel are the core problems, which have a direct relation with the performance of classification, and the research of this field is not enough. In this paper support vector machine (SVM) was used as an example, and the performance of common kernel functions was evaluated through observing and computing the features of kernel matrix. Base on this, a new mixed kernel function was gotten by optimization of kernel functions, and the experimental data proved that the performance of SVM was improved by the mixed kernel function. If the weighting coefficient was selected properly, the correct rate could even reach to 100%. What’s more, not only a method to construct a new learning machine was given, but also a reference for selecting kernel function was given.

Sign in / Sign up

Export Citation Format

Share Document