CBM – Page 2 – TildAlice

Feature Engineering for Predictive Maintenance: Health Indicators

2026년 01월 01일

Extract FFT peaks, RMS trends, and thermal gradients from vibration data for RUL prediction—features that beat raw signals by 40% accuracy.

2025년 12월 31일

Fix the 3 timestamp sync errors that ruin CBM predictions. Trigger-based logging pipeline design with sub-second precision on edge devices.

2025년 12월 05일

Use SHAP and Grad-CAM to prove your CNN bearing diagnosis model is looking at fault frequencies, not noise. Validation workflow included.

2025년 12월 02일

Transfer Learning on 50 images: fine-tune ImageNet CNNs for 94% fault detection accuracy. Beats training from scratch with 1000+ samples.

2025년 11월 11일

Wavelet vs STFT for bearing fault detection: frequency resolution comparison on 10,000 vibration signals. Clear winner emerges.