Model based real-time parameter identification in oscillating systems is a topic of ongoing interest, especially in the context of fault diagnosis during the operation of the system. At the core is a sufficiently small model which is successively calibrated by measurement data. For smooth data such as temperatures, Kalman-based filtering works well. However, for highly oscillatory data from, for example, rotating systems which are often additionally disturbed by harmonic excitations, these methods are prone to failure. In this paper we present an identification method that is able to detect changes in the stiffness properties of the system characterized by a single fault parameter based on frequency data. Its superior performance is demonstrated by a mass–spring system as well as a rotating shaft.
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