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The usefulness of Bayesian network technology for expert-systems for diagnosis, prediction, and analysis of complex technical systems has been shown by several examples in the past. Yet, diagnosis systems using Bayesian networks are still not being deployed on an industrial scale. One reason for this is that it is seldom feasible to generate networks for thousands of systems either by manual construction or by learning from data. In this paper, we present a systematic approach for the generation of Bayesian networks for technical systems which addresses this issue. We use existing system specifications as input for a domain-dependent translation process that results in networks which fulfil our requirements for model-based diagnosis and system analysis. Theoretical considerations and experiments show that the quality of the networks in terms of correctness and consistency depends solely on the specifications and translation rules and not on learning parameters or human factors. We can significantly reduce time and effort required for the generation of Bayesian networks by employing a rules-based expert system for generation, assembly and reuse of components. The resulting semi-automatic process meets the major requirements for industrial employment and helps to open up additional application scenarios for expert systems based on Bayesian networks.

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