Multiscale Ordinal Complexity Analysis for Wind Turbine Bearing Fault Diagnosis - Paulo R. L. Almeida; Thyago L. V. Lima; Alisson V. Brito; Abel C. Lima Filho (International Journal of Prognostics and Health Management, 2026)
Permutation-entropy methods for bearing fault diagnosis in wind turbines are typically validated on laboratory test rigs without enforcing specimen-level separation in cross-validation or quantifying axis-specific diagnostic contributions. This study addresses these limitations using the Fraunhofer LBF operational wind turbine bearing dataset, applying multiscale permutation entropy (MPE) to triaxial front-bearing accelerometer signals across 44 bearing specimens (18 healthy, 10 inner race, 4 outer race, 12 roller element), with two corrupted files excluded following data quality screening. GroupKFold cross-validation with unique specimen-level group identifiers prevents the data leakage that arises when temporally correlated analysis windows are split without regard to bearing identity - a limitation present in all ten studies identified in a systematic literature search. MPE achieves 97.67% +/- 2.10% window-level and 97.73% specimen-level accuracy using 12 features across three accelerometer axes, outperforming weighted permutation entropy (WPE, 91.83% window-level, 95.45% specimen-level) and matching a physically-augmented hybrid (MPE+Physical, 21 features) that contributes no additional specimen-level accuracy despite 33.0% feature importance within the combined set. The X-axis accelerometer (brng.f.x) accounts for 40.7% of classification importance, consistent with the primary radial load direction. Permutation entropy reveals a monotonic complexity hierarchy across fault types - Healthy (mu=0.702) < Roller Element (mu=0.889) < Inner Race (mu=0.981) < Outer Race (mu=0.992) - with Cohen's d > 0.9 for all pairwise comparisons. Roller element faults exhibit bimodal permutation entropy distributions, explained by load-zone-dependent impulsive generation. These results demonstrate that multiscale ordinal pattern analysis captures physically meaningful fault signatures in operational wind turbine data when evaluated under methodologically sound cross-validation protocols.