Skip to Main Content
This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response without the use of a hard real-time operating system. In particular, we focus on the problem of reactively computing fault diagnosis by means of different Bayesian network inference algorithms on non-real-time operating systems where low-criticality (background) process activity and system load is unpredictable. To address this problem, we take in this paper a reconfigurable adaptive control approach. Computation time is modeled using an ARX model where the input consists of the maximum number of background processes allowed to run at any given time. To ensure that the reactive (high-criticality) diagnosis is computed within a set time frame, we introduce a minimum degree pole placement controller to impose a limit on the maximum number of low-criticality processes. Experimentally, we perform electrical power system diagnosis using a Bayesian network model of and data from a NASA electrical power network. The Bayesian network inference algorithms likelihood weighting and junction tree propagation are successfully applied and changed mid-simulation to investigate how inference computation time changes in an unpredictable operating system, as well as how the controller reacts to inference algorithm changes.