Fault Detection in Automobile Engines: A Soft Computing Approach

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 book, fault detection of two stroke petrol engine, four strokes motor cycle engine and four stroke car engine have been explained. 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 soft computing approach for detection of multiple faults in automobile engines is proposed, which include signal conditioning, signal processing, statistical analysis and Artificial Neural Networks.

Autorentext

Dr. Shankar N. Dandare is a professor in Electronics Engineering Department, Babasaheb Naik College of Engineering, Pusad, India. He has completed his doctorate in Electronics Engineering. He is a member of many professional bodies like ISTE, AMIE and IETE. He has total teaching Experience of 32 years and published more than 25 research papers.