Predictive Maintenance via Sensor ML
Budget: ₹750 – ₹1,250 INR
I have continuous sensor streams (temperature, vibration, current, etc.) coming from a set of industrial assets and I need a reliable machine-learning workflow that turns those raw readings into timely maintenance predictions. The goal is clear: forecast when each machine will require attention so downtime is minimized and spare parts can be scheduled in advance.
You will receive a historical data dump (CSV and Parquet) plus a live MQTT feed for validation. After exploring and cleaning the data, build a model—traditional algorithms or deep learning, whichever gives the best performance—that outputs a remaining-useful-life score or binary “service-now” flag for every asset at regular intervals. Python with scikit-learn, TensorFlow or PyTorch is preferred so the final solution stays easy to maintain in our existing stack.
Deliverables
• Notebook or .py script covering preprocessing, feature engineering and training
• Trained model file and clear instructions for re-training with fresh data
• Lightweight REST or CLI interface that accepts new sensor readings and returns the maintenance prediction in real time
• Brief report summarizing feature importance, evaluation metrics and suggested next steps
Acceptance criteria
• F1-score ≥ 0.85 (or better than current rule-based baseline) on a held-out test set
• Code runs end-to-end on Ubuntu 22.04 with a single shell command
• All dependencies listed in a requirements.txt or environment.yml
Timeline is flexible within reason, but daily progress notes in our shared Git repo are essential. Let’s turn these sensor logs into actionable maintenance intelligence.
You will receive a historical data dump (CSV and Parquet) plus a live MQTT feed for validation. After exploring and cleaning the data, build a model—traditional algorithms or deep learning, whichever gives the best performance—that outputs a remaining-useful-life score or binary “service-now” flag for every asset at regular intervals. Python with scikit-learn, TensorFlow or PyTorch is preferred so the final solution stays easy to maintain in our existing stack.
Deliverables
• Notebook or .py script covering preprocessing, feature engineering and training
• Trained model file and clear instructions for re-training with fresh data
• Lightweight REST or CLI interface that accepts new sensor readings and returns the maintenance prediction in real time
• Brief report summarizing feature importance, evaluation metrics and suggested next steps
Acceptance criteria
• F1-score ≥ 0.85 (or better than current rule-based baseline) on a held-out test set
• Code runs end-to-end on Ubuntu 22.04 with a single shell command
• All dependencies listed in a requirements.txt or environment.yml
Timeline is flexible within reason, but daily progress notes in our shared Git repo are essential. Let’s turn these sensor logs into actionable maintenance intelligence.