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A Method of Loudspeaker's Pure Tone Fault Detection Based on Time-Frequency Image Fractal

机译:基于时频图像分形的扬声器纯音故障检测方法

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摘要

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 time-frequency 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 box-counting 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.
机译:世界上大多数扬声器的纯音故障是通过人的听力检测到的。显然,由于主观性不能保证准确性,并且容易引起听觉疲劳。根据扬声器纯音检测的特点,提出将扫频响应信号转换为二维时频图像信号,通过小波包变换增强故障信息的特征。然后通过二值化和边缘提取将时频图像预处理为轮廓。提出了利用图像分形方法对时频图像进行计数的维数,并将其作为扬声器检测的故障特征。通过车间在线实验的验证,将分时维数作为时频图像轮廓的复杂度,可以作为故障判断的特征,故障识别的准确率可以达到95%。它完全可以满足扬声器在线故障检测的要求,并且比其他最新专利要好。

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