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. https://www.roneverhart.com/Ferrofish-A32pro-32-Channel-AD-DA-Converter-with-MADI-and-ADAT-p18115/