In this paper, stochastic models for fault detection in industrial automation processes are investigated. Thereby, nonlinear, time-variant systems are considered. The basic idea consists in building a probability distribution model and evaluating the likelihood of observations under that model. In contrast to the existing methods, this paper considers the practically important case in which measurement noise is negligible and all process variables are observable. This assumption allows the direct evaluation of a probability distribution for fault detection without approximations such as second order statistics or particles. The main part of this paper deals with adequate models for this probability distribution such as Gaussian and Hidden Markov models. Such models require predictions of the expectation values of the respective probability distributions. Regression models such as (multivariate) linear regression models and neural networks are investigated for this purpose. Evaluations are conducted with respect to prediction accuracies and fault detection capabilities of the employed models. Evaluations show superior results of the novel approach compared to existing fault detection methods, which are based on approximations such as second order statistics.
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