Fault Recognition in a Four Stroke Internal Combustion (IC) Engine. an Artificial Neural Network (Ann) Based Approach

Fault Recognition in a Four Stroke Internal Combustion (IC) Engine. an Artificial Neural Network (Ann) Based Approach

Description

Research Paper (postgraduate) from the year 2015 in the subject Engineering - Automotive Engineering, course: Engineering and Technology, language: English, abstract: In recent times, research on effective Acoustic Emission (AE)-based methods for condition monitoring and fault recognition has attracted many researchers. They recognize that the advanced methods of supervision, fault recognition become increasingly important for many technical processes, for the improvement of reliability, safety and efficiency. The use of acoustic signals for fault diagnosis in four-strokes Internal Combustion Engine has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The classical approaches are limited to checking of some measurable output variables and does not provide a deeper insight and usually do not allow a fault diagnosis. Engine problems are caused primarily by improper maintenance or fatigue caused by normal wear and tear and also worn out or clogged vehicle parts. The main cause of overheating of the engine, engine surging and other problems is noticed as worn out parts. The faults in Internal Combustion (IC) engine, reduces the performance, fuel average, smoothness also a change in engine sound is observed. The faults in IC engines can be recognized and repaired based on engine sound and past experience. But as the engines are becoming more and more complex, getting expertise in fault recognition and localization is difficult, so there is a need of assistance system for fault recognition in IC engine, which will tell you about the possible fault based on the data provided to it.


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Details

Author(s)
Shankar Dandare, Mayur R Parate
Format
Paperback | 98 pages
Dimensions
148 x 210 x 6mm | 136g
Publication date
23 Feb 2017
Publisher
GRIN Publishing
Language
English
Edition Statement
2. Auflage
ISBN10
3668396760
ISBN13
9783668396760