Synapse-PM

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Inspiration The goal was to address the costly, disruptive issue of unplanned equipment downtime in industrial settings by building a platform that brings intelligence and automation to maintenance operations

What it does Synapse-PM leverages AI to analyze real-time sensor data, predict machinery failures, and provide actionable recommendations for preventive maintenance, minimizing unexpected breakdowns and optimizing scheduling

How we built it The platform combines LSTM-based machine learning models for RUL prediction, a FastAPI backend for serving results, a Streamlit dashboard for visualization and management, and robust MLOps practices with Docker and Weights & Biases to ensure reliability and reproducibility across deployments.

Challenges we ran into Major hurdles included handling diverse and noisy datasets from different industries, ensuring the AI models generalize across equipment types, optimizing performance for real-time inference, and building secure yet user-friendly interfaces.

Accomplishments that we're proud of Successfully deploying the system in simulations for aerospace, industrial, and healthcare devices, demonstrating transferability and business value, and creating an end-to-end product that bridges data science with real-world operational impact What we learned Gained deep expertise in time series analysis, sensor data processing, industrial MLOps, cross-domain model generalization, and user-centric application design for both technical and non-technical users

What's next for Synapse-PM Future plans involve adding explainable AI features, expanding support for more sensor types, developing automated retraining pipelines, and piloting the platform with industry partners to further validate and refine the solution.

Built With

• api
• dl
• fast
• flask
• lstm
• ml
• python
• streamlite