DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING

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Author: search.filters.author.Waheed, M. A.

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    Diagnosis of gasoline-fuelled engine exhaust fume related faults using electronic nose
    (2010) Arulogun, O. T; Waheed, M. A.; Fakolujo, O. A.; Omidora, E. O.; Olaniyi, O. M. O. M.
    Fault diagnosis, isolation and restoration from failure are crucial for maintenance and reliability of equipment. In this paper, a condition monitoring approach that uses the sense of smell was investigated to diagnose ignition and loss of compression faults in gasoline-fuelled engine. An electronic nose based condition monitoring system was used to obtain smell print of the exhaust fumes of an automobile gasoline engine in different normal and faulty operating conditions. The data were analyzed with fuzzy c-means, hybrid principal component analysis and artificial neural network. Fuzzy C- means clustering was used to ascertain the extent to which the smell prints can characterize the selected engine faulty and normal conditions. Silhouette diagrams and silhouette width figures were used to validate the clusters. The faults considered were all correctly classified by hybrid principal component analysis and artificial neural network algorithm with 100% accuracy.
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    A framework for electronic nose based condition monitoring and diagnosis of automobile engine faults.
    (Nigeria Computer Society, 2009) Arulogun, O. T; Fakolujo, O. A.; Waheed, M. A.; Omidiora, E. O.; Olaniyi, O. M.
    A framework for condition monitoring approach that uses the sense of smell was investigated to diagnose the faults of plug-not-firing, loss of compression and carburettor faults from the exhaust fumes of gasoline fuelled automobile engine. An electronic nose based condition monitoring hardware and software was developed using the framework to obtain smell prints that correspond to normal operating conditions and various induced abnormal operating conditions. Fuzzy C-means and K means clustering were used as exploratory data visualization tools to ascertain if the obtained smell prints from the developed system could characterize the faults considered. The results of exploratory cluster analysis showed that the obtained smell print could typify the faults considered.