Gray Langurs Optimizer-Optimized Feature Mode Decomposition for Adaptive Denoising of Multi-Source Monitoring Data from Floating Offshore Wind Turbines

Abstract

Feature Mode Decomposition (FMD) adaptively decomposes signals into band-limited modes through an adaptive finite impulse response (FIR) filter bank optimized via correlated kurtosis (CK) maximization, yet its denoising performance is highly sensitive to four hyperparameters—the number of decomposition modes nm, the filter length L, the CK shift order M, and the characteristic-period scaling Tscale—whose manual tuning is impractical for multi-channel floating offshore wind turbine monitoring deployments. We propose GLO-FMD, an adaptive denoising framework coupling the Gray Langurs Optimizer (GLO) with FMD. GLO autonomously optimizes the FMD parameters, thereby aligning the CK objective with structural modal periods rather than impulsive fault periods. Although the search space spans (nm,L,M,Tscale), the CK shift order M is fixed at 2 and Tscale is estimated automatically from the dominant autocorrelation peak; consequently, only (nm,L) are actively optimized. The optimized FMD decomposes multi-axis tower-base signals into band-limited modes through iterative CK-maximizing FIR filter optimization; each mode identifies a dominant periodic component, and the original signal is zero-phase band-pass filtered around the identified frequencies to preserve physical phase during reconstruction. Validation employs (i) semi-synthetic signals reproducing the measured tower-base structure (a smooth 0.15 Hz structural mode plus an impulse-excited 3.77 Hz resonance) with exactly known ground truth—a best-case benchmark by construction that isolates denoising capability from reference uncertainty—and (ii) real strapdown inertial sensor data acquired at 8 Hz from the tower-base interface of a floating offshore wind turbine at an operational site in Chinese coastal waters, over a six-day measurement campaign (18–23 April 2023). Six kinematic channels spanning triaxial acceleration (north, up, east) and triaxial velocity (north, up, east) are analyzed, with 200-s (1600-sample) continuous windows extracted for algorithmic evaluation. On the semi-synthetic data, GLO-FMD achieves a 9.6–10.2 dB SNR improvement over default wavelet thresholding against the known ground truth, and the GLO optimization is essential for reliability—the default FMD configuration is unstable across noise realizations, whereas the optimized parameters recover the clean components consistently. GLO-FMD also achieves pseudo-reference-relative SNR gains of 5.3–7.8 dB over default wavelet thresholding across all six real-data channels. Bootstrap resampling over 12 independent segments confirms statistical significance (p<0.001, Cohen’s d>8), and a no-reference smoothness index provides complementary evaluation independent of the pseudo-reference assumption. Multi-day consistency analysis yields coefficients of variation below 5%, demonstrating short-term consistency across the environmental conditions represented in the six-day dataset. The online denoising stage requires approximately 1.5 s per channel, supporting potential deployment on edge-computing hardware at the turbine controller level.