An algorithm to diagnose ball bearing faults in servomotors running arbitrary motion profiles

A multi-order probabilistic approach for Instantaneous Angular Speed tracking debriefing of the CMMNO׳14 diagnosis contest

“…A typical example is provided by the vibrations of bearings under nonstationary regimes, where order analysis has been successfully applied to the signal envelope or to time-frequency energy distributions [15][16][17]. The IAS also turns out essential to resample machine signals in the angular domain in all applications where synchronization to the machine kinematics is essential to correctly identify and analyze transient events in the machine cycle [18][19][20]. The advantage of synchronizing signals to the angular domain has been fully highlighted within the cyclostationary framework, where the notion of machine cycle is central [21,22].…”

Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal

“…Relatively new interesting approach is related to algorithms for searching for informative frequency band [31,33]. Diagnostics under non-stationary load and operating speed condition is discussed in recent papers given by different authors [9,11,21,30,32].…”

A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings

“…[1][2][3][4][5][6][7][8][9]. Typical damages in the REB produce cyclic disturbance in the signal [1,2,5,7,8,10,35], however, usually they are completely masked by the noise [3,4,6,20,36] or other faults [31]. Moreover impulses may have amplitudes that vary in time due to time varying transmission path, load and properties of noise [7].…”

“…Additionally, properties of the signal depend on variation of the rotational speed and external load. Finally, the time varying mixture consists also of time varying response from other parts of machine and other external sources including Gaussian and non-Gaussian noise [36].…”

“…Many examples of using the existing techniques (wavelets, kurtogram and spectral kurtosis, adaptive LMS (least-mean square) filter, optimal Wiener filter, blind extraction of cyclostationary sources, etc. [11][12][13][14][17][18][19]35,36]), may be found, see [8] for review. However, most of known techniques were applied with reference to the laboratory test data and stationary load/speed conditions.…”