WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 9, 2013
Multiple Fault Detection in Typical Automobile Engines: A Soft Computing Approach
Authors: ,
Abstract: Fault detection has gained growing importance for vehicle safety and reliability. For the improvement of reliability, safety and efficiency; advanced methods of supervision, fault detection and fault diagnosis become increasingly important for many automobile systems. Many times, the trial and error approach has been applied to detect the fault and therefore engine may get more damaged instead of getting repaired. To alleviate such type of problem, the idea of sound recording of engines has been suggested to diagnose the fault correctly without opening the engine. In this paper, fault detection of two stroke engine, Hero Honda Passion four strokes and Maruti Suzuki Alto Automobile Engine have been proposed. The objective is to categorize the acoustic signals of engines into healthy and faulty state. Acoustic emission signals are generated from three different automobile engines in both healthy and faulty conditions. The paper proposes soft computing approach for detection of multiple faults in automobile engines which include signal conditioning, signal processing, statistical analysis and Artificial Neural Networks. The Statistical techniques and different Artificial Neural Networks have been employed to classify the faults correctly. Performance of Statistical techniques and ten types of Artificial Neural Networks have been compared on the basis of Average Classification Accuracy and finally, optimal Neural Network has been designed for the best performance.
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Keywords: Artificial Neural Network, Automobile Engine, Classification Accuracy, Fault Detection and Stistical Techniques