🔍 How does STAMM work?
STAMM describes the lifecycle of soft sensors once they are deployed in real industrial environments, such as reactors, fermentation tanks, or production lines. In these processes, some variables can be measured directly, while others remain hidden, expensive, or physically impossible to observe continuously. Soft sensors address this limitation by estimating these unmeasurable variables from available process data.
Once a soft sensor is deployed, it becomes part of the daily operation, similar to a physical instrument. However, unlike hardware sensors, its reliability depends on assumptions about process behavior that may change over time. STAMM does not develop these models; instead, it provides a framework to safely integrate existing soft sensors into live systems and manage them as operational assets.
After deployment, STAMM continuously monitors the behavior of the soft sensor in real time. Rather than assuming it remains valid indefinitely, it treats the model as a living component of the process. It tracks whether incoming data remain consistent with previously observed conditions, how stable the operating environment is, and how the model's outputs evolve over time — similarly to how experienced operators detect subtle changes in a plant before formal alarms are triggered.
Industrial processes naturally evolve due to changes in raw materials, equipment aging, or updated control strategies. When such changes occur, the soft sensor may still produce outputs, but their reliability can degrade. STAMM identifies these situations by detecting shifts in operating regimes, gradual or abrupt process drift, and mismatches between current and historical operating conditions used during model development. From a digital twin perspective, it ensures that the digital representation remains aligned with the physical system.
Not every change requires intervention. STAMM distinguishes between normal process variability and significant deviations that affect model reliability. When confidence in the soft sensor drops below acceptable levels, it signals that maintenance is required, such as recalibration, validation, or retraining, without prescribing how the model should be rebuilt.
Over time, multiple versions of a soft sensor may coexist, each valid under different operating conditions. STAMM tracks this evolution to ensure continuity and traceability across deployments. In this way, it acts as an operational layer connecting physical processes, digital twins, and data-driven soft sensors into a unified monitoring framework.
A note on metadata
When a soft sensor is deployed, it does not exist in isolation. It comes with basic information that describes what the model is, how it was built, and how it is expected to be used. STAMM relies on this kind of practical metadata to manage deployed soft sensors in a consistent way.
In STAMM, metadata is limited to what is directly available from the soft sensor itself, such as:
- model name, version, and identifier,
- model type and architecture,
- software environment and libraries,
- input variables, units, and expected ranges,
- output variables and scaling,
- training setup and basic validation information.
This metadata does not attempt to fully describe the process or replace domain knowledge. Instead, it provides enough structure to:
- clearly identify which soft sensor is deployed,
- understand its expected inputs and outputs,
- track different versions of the same model,
- and support monitoring and maintenance decisions.
In simple terms, metadata helps STAMM answer basic operational questions such as "Which model is running?", "What data does it expect?", and "Is this the same soft sensor as before?"
STAMM uses metadata as a lightweight but essential layer to support reliable deployment and long-term operation of soft sensors — nothing more, and nothing less.