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Objective:
Because of the aging of parts and mechanical structure, the vibration of connecting rod is easy to produce abnormal state, while the abnormal vibration states are difficult to detect in a timely manner,thus resulting in shutdown and material breakage during the tobacco processing process, or uneven tobacco screening, thereby reducing the quality of tobacco products . In order to solve this problem, an automatic detection technology based on Dempster-Shafter (DS) theory is proposed in this paper, so as to deal with the abnormal vibration of connecting rod in time.
Methods:
The image data inside the tobacco sieve vibrating groove were collected, and the Kanade-Lucas-Tomasi (KLT) algorithm was used to track the continuous frames of the image and extract the connecting rod feature points at adjacent moments of the tobacco sieve vibrating groove. A series of microphones are constructed to obtain the location of noise source signals, and the noise source signals are denoised using wavelet coefficients. DS theory was used to fuse the image features and sound signals, calculate the vibration state conflict factor of connecting rod, judge the vibration state of connecting rod according to the conflict factor, and complete the automatic detection of abnormal vibration of connecting rod in the vibration groove of tobacco screen.
Results:
The experimental results show that the proposed method can accurately identify the fault position of the connecting rod and classify the abnormal vibration level, and the results of abnormal vibration amplitude of the connecting rod in the vibrating groove of tobacco screening are highly consistent with the actual results.
Conclusion:
The vibration anomaly automated detection technology designed in this article can be used for the analysis of the state of connecting rod mechanical components in the tobacco industry and the diagnosis of faults.
