METHODOLOGY AND REALIZATION OF A HEAT PIPE PERFORMANCE TEST IN A THERMAL VACUUM CHAMBER


Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine

ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.MethodsFirstly

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