An Approach of Filtering to Select IMFs of EEMD in Signal Processing for Acoustic Emission [AE] Sensors

The pipeline system is the important part in the media transportation for oil and gas transmission but due to weak maintenance, it leads to the corrosion, leakage stresses and mechanical damage of oil and gas pipelines. The signal processing is used to decompose the raw signal and analysis will be in time-frequency domain. Number of existing signal processing methods can be used for extracting useful information. However, the problem of signal processing method, essential to highlight the wanted information and attenuate the undesired signal is trivial. Several signal processing methods have been implemented to solve this issue. Research using Empirical Mode Decomposition (EMD) algorithm shows promising results in comparison to other signal processing methods, especially in the accuracy showing the relationship between signal energy and time frequency distribution by represents series of the stationary signals with different amplitudes and frequency bands. However, this EMD algorithm will still have noise contamination that may compromise the accuracy of the signal processing to highlight the wanted information. It is because the mode mixing phenomenon in the Intrinsic Mode Function's (IMF) due to the undesirable signal with the mix of additional noise. There is still room for the improvement in the selective accuracy of the sensitive IMF after decomposition that can influence the correctness of feature extraction of the oxidized carbon steel. Using two data sets from the Acoustic Emission Sensors [AE], signal processing flows have been presented in this paper. Wave propagation in the pipeline is a key parameter in acoustic method when the leak occurs. More experiments and simulation need to be carried out to get more result for leakage signature and localisation of defect.