Abstract
Mostly pure tone fault of loudspeakers in the world is detected by human hearing. Obviously the accuracy could not be guaranteed due to subjectivity and it is easy to cause auditory fatigue. Based on the characteristics of loudspeaker’s pure tone detection, we propose that the response signal of frequency sweep can be converted into two-dimension timefrequency image signal to enhance the characteristics of fault information through wavelet packet transform. Then time-frequency images are pretreated into contours by binarization and edge extraction. The boxcounting dimensions of time-frequency image by image fractal method is proposed and regarded as the fault characteristics for loudspeaker detection. Through the verification of on-line experiments in workshop, the fractal dimension which regarded as complexity of the time-frequency image contours can be the feature for failure determination, and the fault identification accuracy rate can reach 95%. It fully meets the requirements of loudspeakers fault detection on-line and better than other recent patents.
Keywords: Fault detection, image fractal; the abnormal sound of loudspeaker; time-frequency image fractal, tone fault detection, wavelet packet analysis.
Graphical Abstract
5