Features extraction for characterizing partial-circumferential pipe wall thinning using TM01 mode microwaves

In the inspection of partial-circumferential pipe wall thinning (PCPWT) using mode microwaves, higher-order mode microwaves are excited at various frequencies when the primary mode interacts with the thinning region. This alters the reflection of mode waves, and consequently affects the signals. The behavior of signals varies across different frequency ranges. Therefore, in this study, we analyzed the signals within specific corresponding frequency bands: (1) low frequencies, where higher-order modes have not yet been generated; (2) intermediate frequencies, where the mode is excited but the mode is not yet; and (3) higher frequencies, where both the and mode are excited. In the signal analysis, Singular Spectral Analysis (SSA) was employed to decompose the simulated signal into two components: a slowly oscillating component - exhibiting beating patterns particularly in the low-frequency range - and a residual component, characterized by irregular oscillation attributed to higher-order modes, especially at intermediate and higher frequencies. The results showed that both the thinning thickness and circumferential extent can be characterized using features derived from the two components. In the experimental study, a variety of signal processing techniques have been applied to measurement signals, which include reflections other than from the PCPWT. By using SSA and transforming the measurement signals across various domains – namely, frequency, spatial, and - domains – signals most strongly associated with PWT were successfully extracted. These signals exhibited features consistent with simulation results, validating their potential for characterizing higher order modes and, consequently, PCPWT. • Higher-order mode excitation alters mode reflection and signals. • Use Singular Spectral Analysis to split into slow and rapid oscillating components. • Use features from both components to characterize partial-circumferential wall thinning. • Transform signals across domains to extract PWT-related features.