A Crack Detection Method for Pipelines Using Wavelet-Based Decision-Level Data Fusion

A novel crack detection method using wavelet-based decision-level data fusion is proposed and verified by simulations and experiments. In this work, we established a crack signal model using wavelet functions that fits all crack signal scales without need of windowing, and showed that the signal-to-noise ratio (SNR) difference between the independent signals to be fused has a significant effect on the overall detection performance. Based on this observation, we designed four wavelet-based decision-level data fusion rules. We then presented a detection method where wavelet processing results of individual nondestructive testing (NDT) input are accepted or rejected based on said rules to produce optimized estimation accuracy. To evaluate the proposed method, the first group of simulations was implemented to show that the proposed method identifies the inner and outer surface cracks with a good estimation accuracy; the second and third groups of experiments showed that the proposed method does improve upon individual detection methods alone and has better detection performance than three state-of-the-art methods; finally, the pull-rig experiments verified on our own pipeline inspection gauge (PIG) that the proposed method does improve the detection probability beyond that of individual detection methods, in an actual high-speed pipeline inline inspection (ILI).