Inspiration Industrial downtime due to unexpected machine failures costs businesses nearly $50 billion annually worldwide. This inefficiency motivated us to create an AI-powered predictive maintenance solution that anticipates failures before they occur, maximizing uptime and reducing costly repairs.
What it does Synapse-PM predicts the Remaining Useful Life (RUL) of industrial machinery by analyzing real-time sensor data such as temperature, vibration, and pressure. It provides actionable maintenance timelines, cost estimates, and rescheduling alerts via an intuitive Streamlit dashboard, helping companies shift from reactive fixes to proactive maintenance.
How we built it We developed Synapse-PM using advanced Long Short-Term Memory (LSTM) networks trained on extensive datasets, including NASA turbofan engine data and simulations for hydraulic pumps and MRI machines. The backend is powered by FastAPI serving the model, while user interfaces are built on Streamlit with integrated MLOps tools like Weights & Biases for experiment tracking and Docker for containerization and scalable deployment.
Challenges we ran into Handling diverse datasets with inconsistent formats required building a flexible data preprocessing engine. Ensuring real-time prediction accuracy while maintaining system responsiveness was complex. Deploying a seamless user experience with secure data handling and multi-domain adaptability also posed non-trivial technical and logistical challenges.
Accomplishments that we're proud of We achieved R² prediction scores between 70% and 85%, demonstrating strong accuracy across industries. The platform’s adaptability proved successful in aerospace, manufacturing, and healthcare contexts. We implemented a full-stack solution with end-to-end deployment, integrating robust user authentication and dynamic dashboards for decision-making.
What we learned We gained deep expertise in time-series AI models, MLOps best practices, and cross-industry AI application design. We learned the critical importance of flexible system architecture to handle real-world industrial data variability and the value of user-centric interface design in driving technology adoption.
What's next for Synapse-PM We plan to enhance real-time sensor integration, expand support for additional industrial equipment types, and develop predictive insights beyond RUL such as fault type classification. Building strategic partnerships for pilot deployments and commercial adoption is a key goal. Continual model improvement with ongoing user data will ensure Synapse-PM remains at the forefront of predictive maintenance innovation.
Built With
- across
- ai-driven
- and-postgresql-to-deliver-scalable
- docker
- fastapi
- maintenance
- predictive
- solutions
- streamlit
- synapse-pm-uses-python
- tensorflow/keras
- weights-&-biases
Log in or sign up for Devpost to join the conversation.