TY - GEN
T1 - Quality assessment of engine oil
T2 - 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering, UPCON 2016
AU - Chowdhury, Shambo Roy
AU - Kumar, Ritesh
AU - Kaur, Rishemjit
AU - Sharma, Anupma
AU - Bhondekar, Amol P.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/4/7
Y1 - 2017/4/7
N2 - A.C impedance spectroscopy is a very popular and decisive method to determine certain physicochemical properties of samples. In this work we use classification algorithms to identify the used or fresh class of engine oils, based on their impedance spectra. A proposed equivalent circuit, with multiple combination of resistors, capacitors and constant phase element (CPE), along with raw impedance data constituted the input feature matrix for the classifiers. Classifiers, based on Support Vector Machine (SVM) and Artificial Neural Network applied separately, were able to identify between used samples and fresh samples with an accuracy of about 98 to 100 percent. The qualitative performance of the method was defined by false positive rate, false negative rate, sensitivity rate and specificity rate.
AB - A.C impedance spectroscopy is a very popular and decisive method to determine certain physicochemical properties of samples. In this work we use classification algorithms to identify the used or fresh class of engine oils, based on their impedance spectra. A proposed equivalent circuit, with multiple combination of resistors, capacitors and constant phase element (CPE), along with raw impedance data constituted the input feature matrix for the classifiers. Classifiers, based on Support Vector Machine (SVM) and Artificial Neural Network applied separately, were able to identify between used samples and fresh samples with an accuracy of about 98 to 100 percent. The qualitative performance of the method was defined by false positive rate, false negative rate, sensitivity rate and specificity rate.
KW - artificial neural network
KW - engine oil
KW - impedance spectroscopy
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85018323890&partnerID=8YFLogxK
U2 - 10.1109/UPCON.2016.7894724
DO - 10.1109/UPCON.2016.7894724
M3 - Conference contribution
AN - SCOPUS:85018323890
T3 - 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering, UPCON 2016
SP - 608
EP - 612
BT - 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering, UPCON 2016
PB - Institute of Electrical and Electronics Engineers
Y2 - 9 December 2016 through 11 December 2016
ER -