Fault classification using SVM

Mani Swetha Mandava; Devika Jadhav; Roshan Ramakrishna Naik

doi:10.1109/CircuitsAndSystems.2015.7394056

Fault classification using SVM

Mani Swetha Mandava, Devika Jadhav, Roshan Ramakrishna Naik

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Analog circuits are abundantly used in today's world. Unexpected failures might result in grave repercussions which is why their fault diagnosis is of utmost importance. We put forward an innovative fault classifier technique using Support Vector Machines (SVM) to identify whether the circuit is functioning properly and to identify the fault. We first train the SVM with sample voltages of a simple RLC circuit obtained by simulating this circuit on MATLAB. Fault classification can then be done accurately and precisely by the SVM. Simulations are done on MATLAB to calculate the accuracy and precision of this system.

Original language	English
Title of host publication	Proceeding - 2015 IEEE International Circuits and Systems Symposium, ICSyS 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	17-21
Number of pages	5
ISBN (Electronic)	9781479917310
DOIs	https://doi.org/10.1109/CircuitsAndSystems.2015.7394056
Publication status	Published - 27-01-2016
Externally published	Yes
Event	IEEE International Circuits and Systems Symposium, ICSyS 2015 - Langkawi, Malaysia Duration: 02-09-2015 → 04-09-2015

Conference

Conference	IEEE International Circuits and Systems Symposium, ICSyS 2015
Country/Territory	Malaysia
City	Langkawi
Period	02-09-15 → 04-09-15

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Instrumentation

Access to Document

10.1109/CircuitsAndSystems.2015.7394056

Cite this

@inproceedings{d9c3ca2d106a4a7890ccdad6028de62a,

title = "Fault classification using SVM",

abstract = "Analog circuits are abundantly used in today's world. Unexpected failures might result in grave repercussions which is why their fault diagnosis is of utmost importance. We put forward an innovative fault classifier technique using Support Vector Machines (SVM) to identify whether the circuit is functioning properly and to identify the fault. We first train the SVM with sample voltages of a simple RLC circuit obtained by simulating this circuit on MATLAB. Fault classification can then be done accurately and precisely by the SVM. Simulations are done on MATLAB to calculate the accuracy and precision of this system.",

author = "Mandava, {Mani Swetha} and Devika Jadhav and Naik, {Roshan Ramakrishna}",

year = "2016",

month = jan,

day = "27",

doi = "10.1109/CircuitsAndSystems.2015.7394056",

language = "English",

pages = "17--21",

booktitle = "Proceeding - 2015 IEEE International Circuits and Systems Symposium, ICSyS 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "IEEE International Circuits and Systems Symposium, ICSyS 2015 ; Conference date: 02-09-2015 Through 04-09-2015",

}

Mandava, MS, Jadhav, D & Naik, RR 2016, Fault classification using SVM. in Proceeding - 2015 IEEE International Circuits and Systems Symposium, ICSyS 2015., 7394056, Institute of Electrical and Electronics Engineers Inc., pp. 17-21, IEEE International Circuits and Systems Symposium, ICSyS 2015, Langkawi, Malaysia, 02-09-15. https://doi.org/10.1109/CircuitsAndSystems.2015.7394056

TY - GEN

T1 - Fault classification using SVM

AU - Mandava, Mani Swetha

AU - Jadhav, Devika

AU - Naik, Roshan Ramakrishna

PY - 2016/1/27

Y1 - 2016/1/27

N2 - Analog circuits are abundantly used in today's world. Unexpected failures might result in grave repercussions which is why their fault diagnosis is of utmost importance. We put forward an innovative fault classifier technique using Support Vector Machines (SVM) to identify whether the circuit is functioning properly and to identify the fault. We first train the SVM with sample voltages of a simple RLC circuit obtained by simulating this circuit on MATLAB. Fault classification can then be done accurately and precisely by the SVM. Simulations are done on MATLAB to calculate the accuracy and precision of this system.

AB - Analog circuits are abundantly used in today's world. Unexpected failures might result in grave repercussions which is why their fault diagnosis is of utmost importance. We put forward an innovative fault classifier technique using Support Vector Machines (SVM) to identify whether the circuit is functioning properly and to identify the fault. We first train the SVM with sample voltages of a simple RLC circuit obtained by simulating this circuit on MATLAB. Fault classification can then be done accurately and precisely by the SVM. Simulations are done on MATLAB to calculate the accuracy and precision of this system.

UR - http://www.scopus.com/inward/record.url?scp=84969850589&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84969850589&partnerID=8YFLogxK

U2 - 10.1109/CircuitsAndSystems.2015.7394056

DO - 10.1109/CircuitsAndSystems.2015.7394056

M3 - Conference contribution

AN - SCOPUS:84969850589

SP - 17

EP - 21

BT - Proceeding - 2015 IEEE International Circuits and Systems Symposium, ICSyS 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Circuits and Systems Symposium, ICSyS 2015

Y2 - 2 September 2015 through 4 September 2015

ER -

Fault classification using SVM

Abstract

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this