Transient analysis for leak signature identification based on Hilbert Huang transform and integrated kurtosis algorithm for z-notch filter technique - Muhammad Hanafi, Yusop (2018)
Signal processing is an important tool to analyse non-stationary and non linear data. Many techniques of analysis are available to process non stationary and non-linear data such as FFT, wavelets transform, and demodulation analysis. In a recent study, the analysis of pressure transient signals could be seen as an accurate and low-cost method for leak and feature detection in water distribution systems. Transient phenomena has occur due to the sudden changes in the fluid’s propagation in pipelines system caused by the rapid pressure and flow fluctuation. This is due to events such as closing and opening valves rapidly or through pump failure. Various methods of pressure transient analysis have been applied by several groups of researchers, such as cepstrum analysis, cross-correlation, wavelets analysis, empirical mode decomposition (EMD) and instantaneous frequency analysis. In this research, it is to apply the Hilbert-Huang transform (HHT) as a method to analyse the pressure transient signal. The HHT is a way to decompose a signal into intrinsic mode functions (IMF). However, this method has the difficulty in selecting the suitable IMF for the next data post-processing method, which is Hilbert Transform (HT). Previous researchers normally select the IMF visually, based on the user’s experience, and introduced a merit index that allows the automatic selection of the IMF. The current research presents the implementation of an integrated kurtosisbased algorithm for a z-filter technique (Ikaz) to kurtosis ratio (Ikaz-kurtosis), for this allows automatic selection of the IMF that should be used. This technique is demonstrated on a 67.90-meter medium high-density polyethylene (MDPE) pipe installed with a single artificial leak demonstrator. The results using the Ikaz-kurtosis ratio revealed that the method could be used as an automatic selection of the IMF even though the noise level ratio of the signal is lower. Despite this, the Ikaz-kurtosis ratio method is recommended as a means to implement an automatic selection technique of the IMF for HHT analysis.