On the Classification of Gasoline-fuelled Engine Exhaust Fume Related Faults Using Electronic Nose and Principal Component Analysis

Abstract

The efficiency and effectiveness of every equipment or system is of paramount concern to both the manufacturers and the end users, which necessitates equipment condition monitoring schemes. Intelligent fault diagnosis system using pattern recognition tools can be developed from the result of the condition monitoring. A prototype electronic nose that uses array of broadly tuned Taguchi metal oxide sensors was used to carry out condition monitoring of automobile engine using its exhaust fumes with principal component analysis (PCA) as pattern recognition tool for diagnosing some exhaust related faults. The results showed that the following automobile engine faults; plug-not-firing faults and loss of compression faults were diagnosable from the automobile exhaust fumes very well with average classification accuracy of 91%.