- Collecting, filtrating, analyzing, and sorting data – machine learning requires large amounts of quality data to be trained on.
- Choosing the correct model based on the prediction requirements.
- Training, evaluation and hyperparameter tuning – those steps need to be repeated until accurate prediction is achieved.
Lets see what’s going on with the 3rd phase!
Phase 3: Building, installing and implementing the production solution
After the predictive maintenance solution is implemented on the production enviroment, we still need to gather data. Additionally, we must monitor any information about changes in equipment or its configuration -as this might have an impact on the prediction accuracy. The last phase of that process is production enviroment model maintenance.
Phase 4: Production model maintenance
With the new production data coming in, our model in the testing environment is periodically re-trained and tested against the current model. If its precision is higher, we replace it in the production improving the solution’s accuracy. Older production data become outdated after a time, meaning that we must train the model on more recent data.
Seemingly small changes in the production system can change the prediction accuracy of the model. That’s why it is important to update the production model after modifications of the system.
This process may seem very time-consuming but it’s worth it. The effort will return the investment with quite the margin! How can a company benefit from implementing a predictive maintenance solution?
Reduced maintenance cost
Reduced machine shutdown time for servicing
Reduced risk of safety, health, and quality failures
Extended lifetime of an aging asset
Source: Study conducted by PwC.
AI-based Predictive Maintenance is one of many practical applications of machine learning in the industry. Shortly we will break down more of them in the upcoming articles!