Artificial Intelligence is offering innovative solutions for monitoring, diagnosing and predicting maintenance for equipment.
Unplanned breakdowns in production can cause issues with productivity, increased maintenance costs.
Equipment diagnosis using AI can help identify issues.
How does it work?
An intelligent equipment diagnosis system can utilise a variety of data including:
Temperature data.
Vibration data.
Performance data.
Visual data.
Maintenance history.
Machine learning models use the data to identify any anomalies and potential signs of degradation.
Why multimodal?
Using one source of data may not give you the full picture.
Multimodal means you are using multiple sources of data to help make more informed diagnoses.
For example, vibration along with temperature data can offer more insight when looking at equipment irregularities.
Important Factors to Consider
An AI diagnosis system needs to be tested.
Some key factors to consider:
False alarms.
Missed detection.
Early warning.
Accuracy of diagnosis.
How well it performs under different conditions.
It is also important to remember that whilst you may be able to identify a potential issue, it is important to prove that you have reduced downtime through intervention.
Conclusion
Artificial Intelligence equipment diagnosis can help improve the inspection process and planning of maintenance.
Research into equipment health inspections and quality intelligence shows the importance of intelligent sensing, accurate data and evidence based decision making (Aperture Venture Studio).
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