Cluster-Based Permutation Tests for Automated Identification of Critical Frequency Bands in Vibration Data

Abstract

Condition-based maintenance plus for naval applications requires robust embedded health management systems. However, extracting reliable diagnostic features from reciprocating machinery in harsh maritime environments is hindered by severe acoustic interference and non-stationary dynamics. Traditional point-wise statistical methods, such as analysis of variance, suffer from the multiple comparisons problem and select redundant, highly correlated frequency bins. Conversely, unsupervised dimensionality reduction techniques like principle component analysis are susceptible to broadband noise and lack physical interpretability.
To address these limitations, this paper presents a hierarchical spatial-spectral feature selection framework based on cluster based permutation tests (CBPT). By evaluating multivariate  spectral adjacency and incorporating stratified block bootstrap stability selection, the method isolates deterministic kinematic resonances. This non-parametric approach rejects stochastic background noise and mitigates the large-sample paradox without requiring manual frequency band selection.
The framework was validated using multi-channel vibration data from a reciprocating electromechanical testbed subjected to working fluid leakage, intake restriction, and thermal management degradation. To assess generalization, models were evaluated using a chronological train-test split with progressive levels of additive white Gaussian noise added to the test sets. The CBPT-derived feature space maintains a macro F1-score above 0.90 down to a 5 dB signal-to-noise ratio, outperforming standard univariate and broadband extraction baselines under acoustic interference.  This methodology yields an independent, low-dimensional feature set suitable for autonomous, embedded prognostic architectures.