Automatic parametric fault detection in complex analog systems based on a method of minimum node selection

Abstract The aim of this paper is to introduce a strategy to find a minimal set of test nodes for diagnostics of complex analog systems with single parametric faults using the support vector machine (SVM) classifier as a fault locator. The results of diagnostics of a video amplifier and a low-pass filter using tabu search along with genetic algorithms (GAs) as node selectors in conjunction with the SVM fault classifier are presented. General principles of the diagnostic procedure are first introduced, and then the proposed approach is discussed in detail. Diagnostic results confirm the usefulness of the method and its computational requirements. Conclusions on its wider applicability are provided as well.

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A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

Metrology and Measurement Systems ◽

10.1515/mms-2015-0025 ◽

2015 ◽

Vol 22 (2) ◽

pp. 251-262 ◽

Cited By ~ 9

Author(s):

Chaolong Zhang ◽

Yigang He ◽

Lei Zuo ◽

Jinping Wang ◽

Wei He

Keyword(s):

Analog Circuit ◽

Support Vector ◽

Band Pass Filter ◽

Pass Filter ◽

Low Pass Filter ◽

Op Amp ◽

Novel Approach ◽

Low Pass ◽

Diagnosis Accuracy ◽

Incipient Fault

Abstract Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

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Identification And Classification Of Brain Tumor Images Using Efficient Classifier

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9373.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3677-3683

Keyword(s):

Brain Tumor ◽

Recursive Feature Elimination ◽

Support Vector ◽

Segmentation Method ◽

Pass Filter ◽

Low Pass Filter ◽

Feature Extraction Method ◽

Low Pass ◽

Occurrence Matrix

The main objective of this study is to propose a model for finding brain tumor. Failing to detect the tumor in its prior stage will increases the chance of losing a life. So the identification and treatment for the tumor in its prior stages become vital to save the life of a human being. This work uses the Magnetic Resonance Image (MRI) images to identify and classify the benign and malign type brain tumors. Low pass filter is applied to preprocess the MRI image that removes the unwanted background structures at the same time keeps the important portions sharpened. Watershed segmentation method is used for segmenting the tumor affected area independently. The statistical feature extraction method Gray Level Co-occurrence Matrix (GLCM) is applied to take out the imperative features from the segmented tumor. The feature selection is performed using Recursive Feature Elimination- Particle Swarm Optimization (RFE-PSO) method. Ensemble Support Vector Machine (SVM) is applied to classify the tumors into harmless and harmful from the medical image.

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Overhead Line Fault Location Using Wavelet Packet Decomposition and Support Vector Regression

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.516-517.1396 ◽

2012 ◽

Vol 516-517 ◽

pp. 1396-1399

Author(s):

Chun Guo Fei ◽

Bai Li Su

Keyword(s):

Transmission Line ◽

Support Vector Regression ◽

Fault Location ◽

Wavelet Packet ◽

Support Vector ◽

Wavelet Packet Decomposition ◽

Overhead Line ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass

This paper realizes the fault location in overhead line by using wavelet packet decomposition (WPD) and support vector regression (SVR). All various types of faults at different locations and various fault inception angles on a 735kV-360km overhead line power system are used. The system only utilizes voltage signals with single-end measurements. WPD is used to extract distinctive features from 1/2 cycle of post fault signals after noises have been eliminated by low pass filter. A SVR is trained with features obtained from WPD and consequently used in precise location of fault on the transmission line. The simulation results show, fault location on transmission line can be determined rapidly and correctly irrespective of fault impedance.

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