How do you implement offline AI for predictive maintenance in manufacturing?

Predictive maintenance with offline AI follows a four-stage pipeline: data collection (vibration sensors at 10-50kHz, thermal imaging, current monitoring), feature engineering (statistical features, frequency domain via FFT, time-frequency via wavelets), remaining useful life (RUL) prediction using survival models or LSTMs, and alert generation with maintenance scheduling. Model architecture: use a 1D CNN for vibration pattern recognition, LSTM for degradation trend modeling, and ensemble both for robust RUL estimates. Training requires historical failure data with known failure modes; use SMOTE or GAN-based augmentation if failure examples are scarce. Deploy on edge hardware (Jetson Nano or Raspberry Pi with Coral TPU), processing data locally with sub-second inference. Store predictions in a local SQLite database, sync to central systems when connected. ROI typically shows 25-30% reduction in unplanned downtime and 15-20% reduction in maintenance costs within the first year.