Earticle

Defects in most structures can be generated not only on outside but also on inside or on the back-side during the manufacturing or construction process. Also they cause the growth of defects due to operation of various complex environments and structures will be destroyed eventually. In order to improve the reliability of the structure, the detection and size-estimation of defects should be investigated. In this paper, as an extension of previous studies on surface defects, two-dimensional artificial backside cracks (blind cracks) into paramagnetic material were evaluated by using the same aluminum probe. The potential drop at the defect position is distributed in the n-shape in the case of the back defect, which is different from results of the surface defect (u-shape). The potential drops at the defect position are measured with the largest value. The potential drop ratio () for the defective position is used as a parameter to predict the thickness (l) of defect position.

본 논문에서는 EMD(Empirical Mode Decomposition)를 이용하여 초음파 신호로부터 IMF(Intrinsic Mode Functions)신호들을 분류해낸 후 블라인드 디컨벌루션을 이용하여 시편에 존재하는 결함의 정확한 위치를 검출하는 방법을 제안하였다. 먼저 EMD를 이용해 얻어진 IMF 신호들 중 가장 큰 에너지를 가지는 신호를 선택하여 blind convolution을 적용하였으며, 여기서 얻어진 역 필터 함수를 이용하여 시간 축상에 임펄스 신호를 생성하였다. 제안한 방법의 성능 평가를 위하여 결함이 가공된 시편을 사용하여 실험을 수행하였으며, 저역통과 필터와 RMS를 이용한 결함 위치 검출 방법과 비교하였다. 실험 결과 제안한 방법이 기존 방법보다 더 정확한 결함 위치 검출이 가능함을 확인 할 수 있었다.

In this paper, we proposed new method using empirical mode decomposition and blind deconvolution for defect detection technique of ultrasonic nondestructive evaluation. At first, the maximum energy signal among IMFs obtained from EMD was selected and used by blind deconvolution method. Next, impulse signal was made by inverse filter from using blind deconvolution method in time domain. In order to evaluate the proposed method, the conventional methods were compared using the specimen with artificial defects. From the experiment results, the proposed method can be confirmed that it can detect the defect location more accurate than conventional methods.

Many machine failures are not detected well in advance due to the masking of background noise and attenuation of the source signal through the transmission mediums. Advanced signal processing techniques using adaptive filters and higher order statistics have been attempted to extract the source signal from the measured data at the machine surface. In this paper, blind deconvolution using the eigenvector algorithm (EVA) technique is used to recover a damaged bearing signal using only the measured signal at the machine surface. A damaged bearing signal corrupted by noise with varying signal-to-noise (s/n) was used to determine the effectiveness of the technique in detecting an incipient signal and the optimum choice of filter length. The results show that the technique is effective in detecting the source signal with an s/n ratio as low as 0.21, but requires a relatively large filter length.