A context tree method for multistage fault detection and isolation with applications to commercial video broadcasting systems

Many systems have distributed functionalities among several autonomous components. Such complex systems can generally be described by finite state machines whose behavior is often non-linear and context-dependent. This paper proposes a generic system model based on context trees to predict system behavior for the purpose of fault detection and isolation in a multistage serial system. The approach starts with learning multistage model structures by capturing the expected statistical distribution of the input/output at different stages. The estimated model is then employed to detect departures by comparing the contexts of the new system output with a set of optimal contexts for each stage using the Kullback–Leibler divergence measure. Problems can then be isolated to the stage that contributes the most to these differences. The methodology is demonstrated by an application in commercial video broadcasting systems.

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