Identification of Key Components of CNC Lathe Based on Dynamic Influence of Fault Propagation

Abstract

Identifying the key components of CNC lathe and analyzing the fault propagation behavior is a powerful guarantee for the fault diagnosis and health maintenance of CNC lathe. The traditional key component identification studies are mostly based on the feature parameter evaluation of the fault propagation model, disregarding the dynamics and influence of fault propagation. Therefore, this paper proposes a key component identification method based on the dynamic influence of fault propagation. Based on the CNC lathe architecture and fault data, the cascaded faults are analyzed. The improved Floyd algorithm is used to iterate and transform the direct correlation matrix expressing the cascaded fault information, and the fault propagation structure model of each component is constructed. The coupling degree function is introduced to calculate the dynamic impact degree between components, and the dynamic fault propagation rate of each component is calculated with the dynamic fault rate model. Based on this, the dynamic influence value of fault propagation is obtained by using the improved ASP algorithm. The key components of the system are identified by synthesizing the fault propagation structure model and the dynamic influence value of fault propagation. Taking a certain type of CNC lathe as an example, the proposed method is verified to be scientific and effective by comparing with the traditional identification method of key components based on fault propagation intensity.

A Comprehensive Evaluation Method of Machining Center Components’ Importance Based on Combined Variable Weight

“…The different locations of machining center components cause the influences of propagation to be different. According to the principle of third-order influence [29], faults in machining center components will not only propagate to the adjacent components, but they may also have an impact on the components within the third order. Therefore, the fault propagation probability and the fault propagation risk are introduced into the improved ASP algorithm.…”

Failure prediction of mechanical system based on meta‐action

“…In response to this issue, many scholars have conducted research from the perspective of the whole machine, with the goal of identifying key components. For example, Luan et al 12 have constructed a dynamic identification method for key components from the perspective of mechanical system dynamics. Case studies have shown that this method is superior to traditional fault intensity-based identification methods.…”