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Fault diagnosis in high-speed machining centers (HSM) is critical in manufacturing systems, since early detection saves a substantial amount of time and money. It is known that 42% of failures in these centers occur in rotatory machineries, such as spindles, in which, the bearings are fundamental elements for effective operation. Nowadays, there are several machine- and deep-learning methods to diagnose the faults. To improve the performance of those traditional machine-learning tools, adoi:10.3390/app12042158 fatcat:nvpbct37rvcmtcoe3gu5zncmii