An automated time–frequency approach for ultrasonic monitoring of fastener hole cracks

2007 ◽  
Vol 40 (7) ◽  
pp. 525-536 ◽  
Author(s):  
Adam C. Cobb ◽  
Jennifer E. Michaels ◽  
Thomas E. Michaels
2009 ◽  
Vol 113 (1150) ◽  
pp. 775-788 ◽  
Author(s):  
A. C. Cobb ◽  
J. E. Michaels ◽  
T. E. Michaels

Abstract Ultrasonic nondestructive evaluation methods are routinely used to detect and size fatigue cracks near fastener holes in aircraft structures as a part of scheduled maintenance. In contrast, statistical crack propagation models provide an estimate of the expected fatigue life assuming a known crack size and future fatigue loadings. Here an integrated approach for in situ diagnosis and prognosis of fastener hole fatigue cracks is proposed and implemented that incorporates both ultrasonic monitoring and crack growth laws. The sensing method is an ultrasonic angle beam technique, and cracks are automatically detected from the ultrasonic response. An extended Kalman filter is applied to combine ultrasonically estimated crack sizes with a crack growth law, effectively using the time history of the ultrasonic results rather than only the most recent measurement. A natural extension of this method is fatigue life prognosis. Results from fatigue tests on 7075-T651 aluminium coupons show improved crack size estimates as compared to those obtained from ultrasonic measurements alone, and also demonstrate the capability of predicting the remaining life. This approach for fatigue crack detection, sizing and prognosis is an example of a general strategy for in situ monitoring of structural damage whereby improved results are achieved from the integration of noisy measurements with imperfect crack growth models.


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki

2009 ◽  
Vol E92-B (12) ◽  
pp. 3717-3725
Author(s):  
Thomas HUNZIKER ◽  
Ziyang JU ◽  
Dirk DAHLHAUS

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