Asymmetric-Loss-Guided Hybrid CNN-BiLSTM-Attention Model for Industrial RUL Prediction with Interpretable Failure Heatmaps

This project represents a standard academic approach to Industrial Remaining Useful Life (RUL) prediction. The hybrid architecture (CNN for spatial/sensor correlation, BiLSTM for temporal, and Attention for weighting) is a common 'best practice' pattern in contemporary literature rather than a unique innovation. The use of asymmetric loss is a critical but well-known requirement in safety-critical industrial settings (where underestimating life is better than overestimating). With 0 stars and 1 fork at 2 days old, it lacks any community traction or ecosystem. Defensibility is low because the techniques are widely documented and the 'moat' in this sector usually resides in proprietary sensor data or integration with industrial control systems (ICS), neither of which are provided here. Platforms like AWS Lookout for Equipment or Azure IoT Central are the primary threats, as they increasingly offer AutoML solutions that can replicate these results with lower technical overhead for enterprise users. It is likely to be superseded by more general-purpose time-series libraries (like Darts or GluonTS) or newer Transformer-based architectures specific to industrial time-series.