Reconstruction Assisted Semi-Supervised Conformer for Bolt Looseness Diagnosis in Large-Scale Bolted Mechanical Structures Under Heterogeneous Acquisition Conditions

Le résumé fourni par la source

Bolt looseness diagnosis in power transmission towers is complicated by costly state labels and changing acquisition conditions. This study evaluates a reconstruction-assisted semi-supervised conformer using triaxial vibration-derived MFCCs. Labeled samples supervise binary classification, while labeled and unlabeled samples support denoising and masked reconstruction without state pseudo-labels. A retrospective audit retains 70,409 labeled and 154,394 distinct unlabeled segments and separates 42,421 training, 13,934 validation, and 14,054 test segments by identifiable source-record, inferred-acquisition, and exact-feature groups. Across five seeds, the full model obtains test macro-F1 0.9124 ± 0.0033 and ROC–AUC 0.9692 ± 0.0025, compared with 0.9032 ± 0.0135 and 0.9644 ± 0.0074 for labeled reconstruction. The paired macro-F1 difference is 0.00915 with a 95% seed interval of −0.00503 to 0.02333; reduced-label experiments show no consistent benefit. Performance is substantially weaker on held-out tower types and a later cohort, and waveform-level perturbations cause marked degradation. At clean inference, one encoder call exactly preserves the original duplicated-feature predictions. The results support useful grouped within-archive classification but do not establish physical-tower-independent generalization, annotation savings, or validated field-noise tolerance.

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