Predictive Maintenance
AI-powered prediction of machine failures before they occur to prevent unplanned downtime.
Predictive Maintenance predicts machine failures – reduces unplanned downtime by up to 70% through ML on sensor data.
Explanation
Sensor data is analyzed by ML models: RUL estimation, anomaly detection, survival analysis.
Marketing Relevance
Reduces maintenance costs by 20-50% and unplanned downtime by up to 70%. Critical for Industry 4.0.
Example
Sensors on wind turbines measure vibrations. An LSTM detects bearing wear 3 weeks before failure.
Common Pitfalls
Too few failure data. Wrong sensors or sampling rates. High false positive rate without domain knowledge.
Origin & History
Condition-based monitoring since the 1990s. ML-based from 2015 through IoT and cloud. Today standard in Industry 4.0.
Comparisons & Differences
Predictive Maintenance vs. Preventive Maintenance
Preventive services on schedule; Predictive based on actual condition and prediction.
Predictive Maintenance vs. Anomaly Detection
Anomaly Detection detects current deviations; Predictive Maintenance forecasts future failures.