#Predictive maintenance #Innovation #Automatisation #Condition monitoring
Project's goal
Results
Solution description
One of the main benefits of this project is the possibility of reducing downtime thanks to the early detection of defects. If the prediction is also successful for combined failures and the solution is deployed on real data, maintenance could be significantly improved and equipment downtime minimized.
Another benefit is the finding that training artificial intelligence on simulation data is effective and allows models to be trained with appropriate accuracy. The ability of artificial intelligence to accurately identify fault types and vibration sources has also been confirmed, which is key to planning maintenance and reducing the risk of outages.
Overall, the "PoC SU2025 AI Vibrodiagnostics" project brings new perspectives in the field of vibrodiagnostics and artificial intelligence, and suggests possible improvements in the field of predictive maintenance, innovation and automation.
Benefits and Details
- Reduced downtime – the project showed the potential for early fault detection and downtime prediction, which could significantly reduce equipment downtime and increase plant reliability.
- Training AI simulation data – this hypothesis has been verified and it is clear that appropriately prepared simulation and test data is sufficient to train models with adequate accuracy, but it is necessary to train the models for combinations of failures.
- Checking already processed defects and verifying whether the AI has found them – this hypothesis has been largely confirmed, in the case of individual failures, the detection of the type and source of failures was sufficiently accurate for the considered target expert system.
- Evaluation of new data and their subsequent human control – this hypothesis has been confirmed, the output from the trained AI model for the blind test offers the worker a sufficiently specific and accurate indication of the type of failure and the specific bearing that is the primary source of vibration.
These key benefits of the project suggest the possibilities of using artificial intelligence in the field of vibrodiagnostics to improve predictive maintenance, minimize equipment downtime and increase the efficiency of maintenance planning.
Project solvers