Fault Detection and Classification of Internal combustion Engine (Record no. 14687)
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000 -LEADER | |
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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 |
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 |
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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 |