Predictive maintenance with machine learning

The business case

Unplanned breakdowns are expensive. Industry studies commonly report that predictive maintenance can:

• ✦Cut unplanned downtime by 30–50%
• ✦Lower maintenance costs by 10–40%
• ✦Make equipment last longer

The data you need

• ✦Vibration patterns
• ✦Temperature
• ✦Pressure
• ✦Operating hours
• ✦Past failures

Remaining useful life

Predict how long a part has left:

from sklearn.ensemble import GradientBoostingRegressor

model = GradientBoostingRegressor(n_estimators=100, max_depth=5)
model.fit(X_train, y_rul)

Anomaly detection

Flag readings that don't look normal:

from sklearn.ensemble import IsolationForest

detector = IsolationForest(contamination=0.01, random_state=42)
anomalies = detector.fit_predict(sensor_data)

Putting it to work

• ✦Real-time scoring for critical machines
• ✦Batch predictions for maintenance planning
• ✦A link into the work-order system

Start with the machines where downtime hurts most, show that it pays off, then widen from there.