Fault Detection and Classification of Internal combustion Engine (Record no. 14687)

MARC details
000 -LEADER
fixed length control field 02812nam a22001697a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.43
Item number K963F
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Kumar, Jitendra
245 ## - TITLE STATEMENT
Title Fault Detection and Classification of Internal combustion Engine
Statement of responsibility, etc by Jitendra Kumar
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Electrical Engineering
Year of publication 2017
300 ## - PHYSICAL DESCRIPTION
Number of Pages xii,45p.
Other physical details HB
520 ## - SUMMARY, ETC.
Summary, etc "Numerous attempts have been made in recent years for detecting various faults in an automobile<br/>engine. The aim of developing this technique is to detect the faulty engine more accurately and<br/>reduce the cost of risk for an automobile industry. These developed techniques have potential to<br/>detect and classify the type of fault in the engine automatically. Previously developed techniques<br/>are based on either unsupervised learning or supervised learning. Most of the techniques produced<br/>good results but at the cost of complexity. In the proposed work, an algorithm has been developed<br/>for classifying the healthy and faulty engines and detecting the type of fault (if there is any) using<br/>Support Vector Machines(SVM). SVM has less complexity compared to other methods. SVM has<br/>the ability to classify nonlinear data using a kernel function. A kernel function maps a nonlinear<br/>input data into high dimension space so that this mapped data can be classified linearly.<br/>An acoustic signal is obtained, for a duration of 11 seconds, from a faulty or healthy automobile<br/>engine. Acoustic signals have appropriate information in the frequency domain. This<br/>information is useful for fault detection and classification. Total eighteen statistical features of the<br/>audio signature have been extracted in the frequency domain using Hilbert transform and fed to<br/>the classifier. Classification and detection algorithm is formed by sequential channels of SVM. SVM<br/>works on the principle of Structural Risk Minimization(SRM) which has the ability to reduce confidence<br/>interval and empirical risk. Due to SRM, an over-fitting problem in SVM learning is rarely<br/>seen for a finite data set. Moreover, SVM gives good generalization and performance ability compared<br/>to Artificial Neural Network(ANN). Four faults have been tested on a real time-data. Various<br/>combinations of SVM (using different kernel functions), ANN, and Logistic Regression have been<br/>tested and a maximum accuracy up to 98.17% is achieved by proposed method sequential channels<br/>of SVM, which is better than the previous algorithms on the same data set. The novelty of the proposed<br/>algorithm is in its reduced complexity for the computed features, and the ability to produce<br/>better results."<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Internal combustion Engine
Topical Term MTech Theses
Topical Term Department of Electrical Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Yadav, Sandeep Kumar
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Date acquired Full call number Accession Number Price effective from Koha item type
      Not For Loan Reference S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Course Reserve 2024-01-18 621.43 K963F TM00110 2024-01-18 Thesis