Condition monitoring performed directly from the estimated instantaneous angular speed has found some interesting applications in industrial environments, going from bearing monitoring to gear failure detection. One common way to estimate the angular speed makes use of angular encoders linked to a rotating shaft. At the opposite of traditional time-sampled signals, encoders describe purely angular phenomena often encountered in rotating machines. However, rotating encoders suffer from various geometric defects, corrupting the measurement with an angular periodic signature. The angular synchronous average is a very popular tool to estimate this systematic error, but is only adapted to constant speed conditions, which is rarely the case in real applications. We propose here two different estimators to compute a robust estimation of the synchronous component in variable speed conditions. The former, as a data-driven approach, is based on a local weighted least squares method, while the latter is a model-based approach. We study the behaviour of our estimators with both simulations and experimental signals, and show the relevance of the proposed method in an industrial context.
Condition monitoring Instantaneous Angular Speed Synchronous average Non-stationary
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