Smoothly approximated support vector domain description

Zheng, Songfeng

doi:10.1016/j.patcog.2015.07.003

Cited by 20 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, Equation (17) is realized by Equation (18). If m = 0, the acoustic signal is a normal signal; otherwise, it is abnormal and has m abnormal sub-signals.…”

Section: Principle Of the Model-free Isolation Methodsmentioning

confidence: 99%

“…(2) Diagnosis model. Some classification methods, such as the support vector domain description (SVDD) [17,18], support vector machine (SVM) [19,20] and Bayesian classifier [21], have been successfully applied to pipeline leak detection [22][23][24][25]. However, since model-based methods take the whole signal as the detection object, they can only judge whether it is abnormal and cannot give the local information (number, amplitude, position) of the abnormality in the signal.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals

et al. 2019

View full text Add to dashboard Cite

Pipeline leaks will lead to energy waste, environmental pollution and a threat to human safety. This paper proposes a pipeline leak detection and location method based on the model-free isolation of abnormal (leak and operation) signals. An acoustic signal is first decomposed into “sub-signals” according to its zero-crossing points. Then, based on the definition of signal-to-noise ratio (SNR), the function between the SNR of sub-signal and the number of abnormal sub-signals is established, following which the position of each abnormal sub-signal in the acoustic signal is obtained by tracing its index. Based on this and the cross-correlation analysis, the operation sub-signals can be filtered, which is helpful for the precise leak location. The experimental results demonstrate the computational efficiency and lower false/missing alarm rate of the proposed method that provides an innovative solution for pipeline leak detection.

show abstract

“…In this paper, Equation (17) is realized by Equation (18). If m = 0, the acoustic signal is a normal signal; otherwise, it is abnormal and has m abnormal sub-signals.…”

Section: Principle Of the Model-free Isolation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The fault sample partitioning results are shown in Table 1. The receiver operating characteristic (ROC) [21] curve of each one-classifier is shown in Fig. 5.…”

Section: One-classifier Experimentsmentioning

confidence: 99%

“…5, the performance of one-classifiers for fusion signal are higher than or equal to the one for speed signal and control signal, except the fault severity being 0% case, in which case, the performance of the one-classifier for fusion signal is slightly lower than the one for speed signal. To quantitatively describe the performance of one-classifiers, the areas under the curves (AUCs) [21] of different one-classifiers are calculated. The results are shown in Table 2.…”

Section: One-classifier Experimentsmentioning

confidence: 99%

Thruster Fault Identification for Autonomous Underwater Vehicle Based on Time-Domain Energy and Time-Frequency Entropy of Fusion Signal

Yin

Lin

Tang

et al. 2019

Intelligent Robotics and Applications

View full text Add to dashboard Cite

In order to deal with the problem of low classification accuracy in thruster fault identification based on single fault feature, a fault identification algorithm based on time-domain energy and time-frequency entropy of fusion signal is proposed. Firstly, the fault singular signals from two single aspects, such as surge speed and control voltage, are fused into a fusion signal to reflect fault information more comprehensively. Then, the peak region energy feature of the fusion signal is extracted in time domain, and the entropy feature of the fusion signal is extracted in time-frequency domain, so as to obtain the multidomain fault features. Finally, based on support vector data description algorithm, a multi-classifier is established, and the relative distance between fault sample and each hypersphere in the multi-classifier is calculated. The fault severity corresponding to the fault sample is determined by the minimum relative distance. The experimental results of an experimental prototype in a pool show that the classification accuracy of the proposed method is 95.2%. In comparison to the classification models corresponding to the surge speed and control voltage, the classification accuracy is increased by 5.2% and 22.8% respectively.

show abstract

“…A support vector machine (SVM) has been introduced to solve the outlier detection problem because of its advantages in binary classification. The support vector data description SVDD is a single classification method of support vector machine, which does not need any distribution assumptions for target data, can map the original data to high-dimensional feature space, establish the smallest hypersphere containing the given data, and can detect outliers [20] [21].…”

Section: Introductionmentioning

confidence: 99%

Abnormal Data Detection of Guidance Angle Based On SMP-SVDD For Seeker

Liang

Zhang

Cui

et al. 2021

Preprint

View full text Add to dashboard Cite

The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, aiming to detect abnormal data in seeker pitch angle deviation data, a method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed to detect abnormal data in guidance angle data. On the one hand, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, and the particle swarm optimization is used to find the optimal kernel, which improves the detection accuracy. On the other hand, the constrained quadratic programming problem is smooth and differentiable, and the conjugate gradient method can be applied to reduce the complexity of problem solving. Through simulation experiments, it is verified that the SMP-SVDD method has higher detection accuracy and faster calculation speed compared with different detection methods in different guidance stages.

show abstract

Smoothly approximated support vector domain description

Cited by 20 publications

References 26 publications

Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals

Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals

Thruster Fault Identification for Autonomous Underwater Vehicle Based on Time-Domain Energy and Time-Frequency Entropy of Fusion Signal

Abnormal Data Detection of Guidance Angle Based On SMP-SVDD For Seeker

Contact Info

Product

Resources

About