Title :
Smartphone application for fault recognition
Author :
Verma, Nishchal K. ; Singh, Sushil ; Gupta, J.K. ; Sevakula, Rahul K. ; Dixit, Sudhaker ; Salour, Al
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol., Kanpur, Kanpur, India
Abstract :
Smart-phones have become an integral part of our daily life. Even though smart-phone app development has boomed, only few general purpose applications exist for feature extraction, feature selection and classification of audio data from a smart-phone. The paper presents detailed theory behind the data mining model used, which has given good results on MATLAB. The application was made to learn different fault states of an industrial air compressor. The application was tested to recognize the fault state in real time as the air compressor was running. It has performed very well with classification accuracies above 93.73%. It is believed that similar application and model with some minor changes in specifications can be used for acoustic pattern recognition in wide range of fields; even in industry.
Keywords :
data mining; feature extraction; mathematics computing; mobile computing; operating systems (computers); smart phones; support vector machines; Matlab; SVM; acoustic pattern recognition; audio data; data mining model; fault state recognition; feature classification; feature extraction; feature selection; industrial air compressor; smartphone application development; support vector machine; android; feature classification; feature extraction; feature selection; support vector machines;
Conference_Titel :
Sensing Technology (ICST), 2012 Sixth International Conference on
Conference_Location :
Kolkata
Print_ISBN :
978-1-4673-2246-1
DOI :
10.1109/ICSensT.2012.6522593
