Online early warning method for motorized-spindle degradation without failure data

Abstract

Early warning for the performance degradation of motorized-spindle can effectively prevent machine tool failure and improve machining accuracy and production efficiency. This study proposes an online motorized-spindle early warning method without failure data. First, vibration data are collected and then processed by a Butterworth band-stop filter algorithm. Second, the idle condition of motorized-spindle can be identified online based on the frequency match strategy. Subsequently, an early warning method is proposed to obtain an appropriate first degradation time (FDT). Finally, experimental results show that the idle condition identification accuracy is 99.16% and FDT identification error is 0.61%, which indicates that the proposed method can obtain accurate early warning results of motorized-spindle without failure data.