Definition:
A proactive maintenance strategy that uses AI and IoT data to identify and prevent equipment failures before they occur.
Key Components:
- IoT Sensors: Collect real-time data on equipment performance.
- Machine Learning Models: Analyze data to predict failures.
- Automated Alerts: Notifies operators of potential breakdowns.
Use Cases/Industries:
- Power Plants: Reduces unplanned outages in generation facilities.
- Transmission & Distribution: Identifies failing grid components before they cause outages.
- Industrial Energy Systems: Ensures continuous operation of energy-intensive manufacturing.
Advantages:
- Reduces Downtime: Minimizes unexpected failures and costly repairs.
- Optimizes Maintenance Scheduling: Avoids unnecessary maintenance while preventing breakdowns.
- Extends Equipment Lifespan: Improves asset utilization.
Challenges:
- High Initial Investment: Requires sensor deployment and AI training.
- Data Management Complexity: Needs robust analytics infrastructure.
Related Terms: Condition-Based Maintenance, Smart Monitoring, Industrial IoT
Example:
A wind farm implemented predictive maintenance, reducing turbine failures by 20% and improving overall energy production.
Visited 1 times, 1 visit(s) today
Synonyms:
AI-Powered Maintenance, Proactive Equipment Monitoring, Smart Asset Management
