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The core of the Pump Diagnostics System is a CNN-based deep learning model trained to interpret SRP dynamogram patterns and support pump condition assessment in a more consistent way. It is designed as industrial AI, not as an isolated notebook model, so the focus is on usable diagnostic behavior under real operational constraints.

The full solution is conceived as an end-to-end production pipeline that connects signal preprocessing, model inference, confidence-aware interpretation, and operator-facing review inside a broader production monitoring platform. That means the model is only one layer of the system, while the real value comes from the full decision-support flow around it.

The Streamlit application linked here is a demo and proof-of-concept layer that shows how the diagnostic logic behaves in practice. It is useful for demonstrating the workflow, but the intended production role is as part of a larger engineering platform rather than as a standalone toy interface.