Pipeline instrument signal processing and feature extraction based on VMD kernel principal component analysis

To address the nonstationary, strong noise, and nonlinear coupling of pipeline instrumentation signals, resulting in inseparable features and unstable measurements, this paper proposes a kernel principal component analysis (KPCA) feature extraction method based on variational mode decomposition (VMD). This method first uses VMD to decompose the original signal into several intrinsic modes, suppressing modal aliasing and achieving band-wide denoising. KPCA is then applied to the reconstructed signal or IMF statistical features to obtain a low-dimensional embedding that captures the nonlinear manifold structure. For quantitative evaluation, root mean square error (RMSE) and signal-to-noise ratio (SNR) metrics are used to assess the effectiveness of the proposed method. Experiments are conducted in two stages. First, robustness is verified on a controllable simulated signal (impulse perturbation). Subsequently, the proposed method is compared with PCA and wavelet methods on measured pipeline instrumentation signals. Results show that the proposed VMD-KPCA exhibits robustness advantages in both denoising reconstruction and feature separability, achieving improved SNR and reduced RMSE under multiple operating conditions while maintaining robustness.