Facing to problems which are difficult to test defects and strength of adhesive bonds using traditional ultrasonic nondestructive testing(UNDT)me- thod,modern signal processing and UNDT are combined to solve the problems.
Due to the influences of various noises, the signal-to-noise ratio (SNR) and reliability of digital signal are low in ultrasonic non-destructive testing, and it is one of difficult tasks in NDT fields.
This paper is around wood inner-defect detecting technology, ultrasonic non-destructive testing for wood defects is studied using the energy spectrum variety of the ultrasonic signal by wavelet transform, coefficient of wavelet node and the Artificial Neural Network (ANN).
The seismic surface waves are widely used to infer the properties of the media in different fields ranged from global seismology and geotechnical engineering to ultrasonic Nondestructive test(NDT).
In the ultrasonic nondestructive test of pipeline,there is much defect information in the ultrasonic echo-signal of pulse reflection,but the echo-signal is disturbed by electronic noise,structure noise and so on. Before analyzed,the ultrasonic defect echo signal must be denoised to get better results.
Korolev, the oldest worker of the Central Research Institute for Heavy Machinery (TsNIITMASh), has told about some episodes on the initial stages of the ultrasonic NDT development.
Today, the TOFD procedure is used for operational inspections or quality control of structures during production instead of routine radiography and ultrasonic NDT procedures [1].
A novel method of time-frequency analysis, wavelet-packet atomic decomposition based on the matching-pursuit (MP) algorithm, is proposed for improvement of ultrasonic flaw detection during ultrasonic NDT.