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The analysis of the periodic components in noise and vibration signals measured on rotating equipment, like car power trains, must more and more be done under rapid changes of an axle, or reference RPM. Normal tracking filters (analog, or digital implementations) have limited resolution in such situations; wavelet methods, even when applied after resampling the data to be proportional to an axle RPM, must compromise between time and frequency resolution. The authors propose the application of nonstationary Kalman filters for the tracking of periodic components in such noise and vibration signals. These filters are designed to track accurately signals with a known structure among noise and signal components of different, ‘unknown’, structure.