• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.4
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• pp.290-298
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• 2014

Ultrasonic thickness measurement is a non-destructive method to measure the local thickness of a solid element, based on the time taken for an ultrasound wave to return to the surface. When an element is very thin, it is difficult to measure thickness with the conventional ultrasonic thickness method. This is because the method measures the time delay by using the peak of a pulse, and the pulses overlap. To solve this problem, we propose a method for measuring thickness by using the power cepstrum and the minimum variance cepstrum. Because the cepstrums processing can divides the ultrasound into an impulse train and transfer function, where the period of the impulse train is the traversal time, the thickness can be measured exactly. To verify the proposed method, we performed experiments with steel and, acrylic plates of variable thickness. The conventional method is not able to estimate the thickness, because of the overlapping pulses. However, the cepstrum ultrasonic signal processing that divides a pulse into an impulse and a transfer function can measure the thickness exactly.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.593-596
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• 2004

Hub bearings not only sustain the body of a car, but permit wheels to rotate freely. Excessive radial or axial load and many other reasons can cause defects to be created and grown in each component. Therefore, vibration and noise from unwanted defects in outer-race, inner-race or ball elements of a Hub bearing are what we want to detect as early as possible. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing signal has periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.1093-1096
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• 2004

Bearings are elementary machinery component which loads and do rotating motion. Excessive loads or many other reasons can cause incipient faults to be created and grown in each component. Moreover, it happens that incipient faults which were caused by manufacturing or assembling process' errors of the bearings are created. Finding the incipient faults as early as possible is necessary to the bearings in severe condition: high speed or frequently varying load condition, etc. How early we can detect the faults has to do with how the detection algorithm finds the fault information from measured signal. Fortunately, the bearing fault signal makes periodic impulse train. This information allows us to find the faults regardless how much noise contaminates the signal. This paper shows the basic signal processing idea and experimental results that demonstrate how good the method is.