Rotational frequency estimation from sensed vibrations based on a supervised learning method

Apparatus and associated methods relate to generating a frequency spectrum weighting function for use in estimating a rotational frequency of the rotating member. The estimation of the rotational frequency is based on vibrations sensed by an accelerometer remotely located from a rotating member. The frequency spectrum weighting function is generated by a supervised learning method. The method includes receiving a set of test vectors. The test vectors include a rotational frequency value of the rotating member and a vibrational frequency spectrum corresponding to vibrations propagated to the accelerometer. The vibrations include vibrations caused by the rotating member rotating at the rotational frequency. The method includes calculating a test weighting function, and then weighting the vibrational frequency spectra by the test weighting function. The method includes calculating a vector score indicative of whether the weighted vibrational frequency spectra promote the identification of the rotational frequency of the rotating member.