Multivariate failure prognosis of cutting tools under heterogeneous operating conditions

Failure risk prognosis is indispensable to predict the remaining useful life (RUL) of cutting tools, thereby improving the timely maintenance and boosting the productivity of manufacturing systems. However, the heterogeneity of working conditions is holding back this target. Traditional methods do not discern lifetime data from heterogeneous working conditions but rather aggregate these data for parameter estimation. As such, most of the existing methods become inflexible and cannot adequately handle dynamic and heterogeneous working conditions. Therefore, this paper presents a novel knowledge-driven prognostic framework to integrate the physical feature-based classification model of homogeneous working conditions with the failure risk prognosis of RUL. This new framework effectively identifies and categorizes various types of working conditions with a similarity-evaluation method. Further, a multivariate model integrating lifetime variabilities under homogeneous conditions and real-time prior information is proposed for fault risk and RUL prognosis. This work provides a novel prognostic approach for future risks even with the uncertainty of working conditions. Finally, a case study with degradation datasets of milling insert in the machining center is performed to evaluate and validate the effectiveness of the proposed framework.