Multiple adaptive mechanisms for data-driven soft sensors

- Data-driven soft sensors often include multiple mechanisms for their adaptation.
- Deploying these adaptive mechanisms in prescribed order is often inferior.
- Flexible order of adaptive mechanisms leads to a higher predictive accuracy.
- Cross-validation is successfully used as a basis for the adaptation choice.
- Retrospective adaptation for model optimization further boosts performance.

Recent data-driven soft sensors often use multiple adaptive mechanisms to cope with non-stationary environments. These mechanisms are usually deployed in a prescribed order which does not change. In this work we use real world data from the process industry to compare deploying adaptive mechanisms in a fixed manner to deploying them in a flexible way, which results in varying adaptation sequences. We demonstrate that flexible deployment of available adaptive methods coupled with techniques such as cross-validatory selection and retrospective model correction can benefit the predictive accuracy over time. As a vehicle for this study, we use a soft-sensor for batch processes based on an adaptive ensemble method which employs several adaptive mechanisms to react to the changes in data.