AI-based predictive maintenance uses a variety of data from IoT sensors embedded in equipment, data from manufacturing operations, environmental data, and more to determine which components should be replaced before they break down.
AI models can look for patterns in data that indicate failure modes for specific components or generate more accurate predictions of the lifespan for a component given environmental conditions.
The aim is to avoid unexpected equipment failure and minimize downtime by performing maintenance only when it is necessary. As a result, it can help businesses optimize the overall function of machines.
Methods
- Time-Series Analysis
- Regression Analysis
- Classification
- Clustering
- Anomaly Detection
Challenges
- Data Quality and Availability
- Technical Complexity
- Integration with Existing Systems
- Model Validation and Maintenance
