Virtual Stochastic Sensors: How to Gain Insight into Partially Observable Discrete Stochastic Systems

Claudia Krull, Robert Buchholz, and Graham Horton


virtual stochastic sensor, discrete stochastic model, state spacebased simulation, time series analysis


This paper introduces the idea of a Virtual Stochastic Sensor. This paradigm enables the analysis of unobservable processes in discrete stochastic systems. Just like a virtual sensor, we use physical sensor readings to deduce the value of the quantity of interest. However, both the physical sensor readings and their relationship with the quantity of interest are stochastic. Therefore the measurement of our virtual stochastic sensor is a statistical estimate of the true value. We describe a method to compute the result of the virtual stochastic sensor and show its validity and real-time capability for two example models. We also give system properties that must apply in order for the feasibility of virtual stochastic sensors, such as the sensitivity of the physical sensor output to changes in the quantity of interest. The future potential of virtual stochastic sensors is their variability. They can be used to gain insight into hidden processes of partially observable systems, using readily available data. They enable online monitoring of production lines using already recorded data to ensure optimal control and maximum production efficiency.

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