(untitled) (Texas Journal of Engineering and Technology, 2024)
Early changes in transformer winding geometry often appear as weak mechanical and acoustic signatures before they develop into insulation breakdown, severe inter-turn short circuit or service failure. This paper presents a non-invasive vibro-acoustic deep learning framework for early winding-fault detection. Synchronized vibration and acoustic recordings were obtained from a controlled 15 kVA, 11/0.415 kV, 50 Hz oil-immersed laboratory transformer under healthy operation and four emulated early-fault states. Vibration windows were represented as continuous wavelet transform scalograms, while acoustic windows were represented as Mel-spectrograms. A dual-branch CNN-BiLSTM model with additive attention was used to learn each sensing channel before feature-level fusion and five-class classification. The dataset comprised 600 raw recording runs and 7,200 synchronized windows. The split was performed at raw-run level before segmentation-based learning to prevent leakage among overlapping windows. Across five independent training runs, the fusion-attention model achieved 98.92 ± 0.24% accuracy, 98.61 ± 0.27% precision, 98.47 ± 0.29% recall and 98.54 ± 0.26% F1-score on the held-out test set. The results indicate that paired vibration and acoustic evidence improves early recognition of winding looseness, axial displacement, radial deformation and incipient inter-turn weakness under the controlled test conditions. The method provides a laboratory-validated alarm layer whose substation use should be checked against established maintenance evidence from frequency-response analysis, dissolved-gas analysis, thermal inspection and expert review