000			02812nam a22001697a 4500
082			_a621.43 _bK963F
100			_aKumar, Jitendra _926099
245			_aFault Detection and Classification of Internal combustion Engine _cby Jitendra Kumar
260			_aIIT Jodhpur _bDepartment of Electrical Engineering _c2017
300			_axii,45p. _bHB
520			_a"Numerous attempts have been made in recent years for detecting various faults in an automobile engine. The aim of developing this technique is to detect the faulty engine more accurately and reduce the cost of risk for an automobile industry. These developed techniques have potential to detect and classify the type of fault in the engine automatically. Previously developed techniques are based on either unsupervised learning or supervised learning. Most of the techniques produced good results but at the cost of complexity. In the proposed work, an algorithm has been developed for classifying the healthy and faulty engines and detecting the type of fault (if there is any) using Support Vector Machines(SVM). SVM has less complexity compared to other methods. SVM has the ability to classify nonlinear data using a kernel function. A kernel function maps a nonlinear input data into high dimension space so that this mapped data can be classified linearly. An acoustic signal is obtained, for a duration of 11 seconds, from a faulty or healthy automobile engine. Acoustic signals have appropriate information in the frequency domain. This information is useful for fault detection and classification. Total eighteen statistical features of the audio signature have been extracted in the frequency domain using Hilbert transform and fed to the classifier. Classification and detection algorithm is formed by sequential channels of SVM. SVM works on the principle of Structural Risk Minimization(SRM) which has the ability to reduce confidence interval and empirical risk. Due to SRM, an over-fitting problem in SVM learning is rarely seen for a finite data set. Moreover, SVM gives good generalization and performance ability compared to Artificial Neural Network(ANN). Four faults have been tested on a real time-data. Various combinations of SVM (using different kernel functions), ANN, and Logistic Regression have been tested and a maximum accuracy up to 98.17% is achieved by proposed method sequential channels of SVM, which is better than the previous algorithms on the same data set. The novelty of the proposed algorithm is in its reduced complexity for the computed features, and the ability to produce better results."
650			_aInternal combustion Engine _926100
650			_aMTech Theses _926101
650			_aDepartment of Electrical Engineering _926102
700			_aYadav, Sandeep Kumar _926103
942			_cTH
999			_c14687 _d14687