Predictive Maintenance with AI: 5 Real-World Patterns
Predictive maintenance is the most cited industrial AI use case. The gap between "we're doing predictive maintenance" and "we're saving real money from predictive maintenance" is wide. Here are 5 patterns we've shipped that work.
Key takeaways
- Anomaly detection: simplest, most broadly applicable.
- Remaining Useful Life (RUL) estimation: powerful, data-hungry.
- Classification (failure mode prediction): useful when failure modes are known.
- Rule-based with ML layer: realistic hybrid.
- Vibration analysis specific: mature, ML-enhanced.
Pattern 1: Anomaly detection (unsupervised)
You don't have labeled failure data, but you have lots of normal-operation data. Train an autoencoder or use statistical methods to detect when sensor readings deviate from normal. Flag for inspection.
Works for: most equipment with continuous sensor data. Low data requirements (just normal operation).
Limits: doesn't predict when failure will occur; just that something is unusual.
Pattern 2: Remaining Useful Life (RUL)
Train a regression model on failure-time-series data to predict how much life remains. Output: "60% confidence equipment fails in next 4-8 weeks."
Works for: equipment with well-instrumented failure histories.
Limits: needs labeled failure data, often hard to collect.
Pattern 3: Failure mode classification
When equipment fails, classify the failure mode (bearing wear, motor overheating, alignment issue). Train classifier on labeled data.
Works for: equipment with multiple distinct failure modes, well-documented.
Limits: requires labeled data per failure mode.
Pattern 4: Rule-based with ML hybrid
Domain experts define rules ("if vibration > X AND temperature > Y for > 5 mins, alert"). ML layer adds nuance (varies thresholds by operating conditions, time-of-day, etc.).
Works for: equipment where domain experts have strong rules-of-thumb.
Limits: requires domain expertise; pure ML may eventually outperform.
Pattern 5: Vibration analysis (specialized)
For rotating equipment (motors, pumps, compressors), frequency-domain analysis (FFT) of vibration is the dominant predictive signal. ML on top of FFT features detects specific failure modes (bearing wear, imbalance, misalignment).
Works for: rotating equipment universally.
Limits: requires vibration sensors.
What works for production
Start simple
Anomaly detection first. Pattern 1. Get the data pipeline working.
Build labeled data over time
Document every alert and outcome. Build the dataset that lets you graduate to Pattern 2 or 3.
Combine with traditional maintenance
PM doesn't replace scheduled maintenance entirely. It augments.
Trust calibration
Initial models will have many false positives. Build trust gradually.
Common pitfalls
Demo to production gap. Demos on clean data; production has dirty data.
No baseline. Without baseline of current maintenance costs and downtime, ROI is unprovable.
Over-reliance on PM. Equipment still needs scheduled checks.
Privacy/IP. Sensor data from your equipment might be sensitive.
What we recommend
Pick one critical equipment type. Instrument it. Start with anomaly detection. Build labeled data. Graduate to RUL when data supports. Measure ROI from day one.
FAQs
ROI? 5-25% maintenance cost reduction reported when done well.
Data quantity? Anomaly detection: months. RUL: years of failure data ideal.
Edge vs cloud? Edge for real-time alerting; cloud for training.