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/Clos-Pegase-Cabernet-Sauvignon-Hommage-Tenma-Vineyard-Napa-Valley-2014/