This paper develops a generic degradation model based on Dynamic Bayesian Networks (DBN) which predicts the condition of a technical system. Besides handling bi-directional reasoning, a major benefit of this degradation model using a DBN is its ability to adequately model stochastic processes as well as Markov chains. We will assume that the behavior of the degradation can be represented as a P–F-curve (also called degradation or life curve). The model developed here is able to combine information from expert knowledge, any kind of sensor and operating data as well as information from the machine operator. Using the Bayesian approach, uncertain knowledge can be handled appropriately. Thus it is even possible to take into account the environment and stress under which the component or system is operating. Hence, it is possible to detect potential failures at an early stage and initiate appropriate remedy and repair strategies prior to catastrophic failure

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