The Business Case for Predictive Maintenance
Unplanned equipment downtime costs industrial companies an estimated $50 billion annually. Predictive maintenance uses sensor data and ML to predict failures before they happen.
Data Collection Architecture
Sensors - Vibration, temperature, pressure, current, acoustic emission. Choose sensors based on the failure modes you're predicting.
Edge Gateway - Collect high-frequency sensor data, perform initial processing, and transmit to the cloud. Handle connectivity interruptions with local buffering.
Cloud Pipeline - Stream processing for real-time alerts, batch processing for model training.
Feature Engineering
Raw sensor data isn't useful for ML models. Engineer features like:
Rolling averages and standard deviations
Frequency domain features (FFT analysis for vibration data)
Rate of change and trend indicators
Time since last maintenance event
Operating condition context (load, speed, environmental factors)
Model Selection
Remaining Useful Life (RUL) prediction - Regression models (LSTM, gradient boosting) that estimate time until failure. Best when you have run-to-failure data.
Anomaly Detection - Unsupervised models (Isolation Forest, autoencoders) that detect unusual patterns. Best when failure examples are rare.
Classification - Models that classify equipment condition (healthy, degraded, critical). Best for clear condition boundaries.
Deployment
Models run on edge devices for real-time inference (latency-critical) or in the cloud for batch analysis
A/B test models against rule-based alerts to validate improvement
Retrain models regularly as equipment ages and conditions change
Dashboard for maintenance planners showing predicted failures, confidence levels, and recommended actions
Conclusion
Predictive maintenance is one of the highest-ROI applications of IoT and ML. Start with one critical equipment type, prove the concept, then scale to the full fleet.