Tip-timing is a technique for measuring rotating blades vibrations in operation. Its concept exists since the early 70s but it has been more experimented in the last decade through improvements in hardware and software capabilities. It consists of a set of sensors mounted on stator casings that record blade passing times. Then, from this measurement, blade vibrations can be estimated.

Tip-timing technique presents several advantages compared to usual mean of measurement: strain gages. Indeed, installation is easier, it is non-intrusive and all the blades can be monitored.

However, resulting sampling depends on physical configuration i.e. number of sensors. In practice, as the number of sensors is limited, sampling rate is low in relation to the physical observed frequencies and do not respect the Shannon criterion. Thus, it generates important aliasing effects in spectrum, which makes the analysis difficult. In fact, such measurements aim to lead to pseudo-blind analysis, especially for asynchronous vibrations, when there is no hypothesis of underneath structural model.

This main problem of aliasing is partially softened by using a minimum variance spectral estimator that iteratively reduces non-physical content in spectrum, but pseudo-blind analysis remains complicated.

This paper presents a comparison of several methods based on different multisampling averaging concepts for reducing aliasing. Multisampling averaging consists in averaging spectrums from different sampling patterns of the same original signal, so that only stable physical content remains in the final spectrum. This study is presented on different industrial test cases of blade vibrations.

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