Fault propagation analysis of computer numerically controlled machine tools

Computer numerically controlled machine tools are the key equipment of the intelligent manufacturing industry, unscheduled shutdowns can substantially cause equipment damage and production loss. Fault propagation analysis, as a prominent basis of reducing unscheduled downtime, can locate the harmful parts and reveal the fault propagation path. To improve reliability, this paper proposes a novel fault propagation analysis method that can accurately and timely evaluate fault propagation coefficients of components in interior computer numerically controlled machine tools. To depict the latent cause-effect relationships of components, a quantitative causal diagram is built with the aid of design information and topology. An inverse PageRank algorithm is proposed to assess the fault propagation risks of components. Unlike the traditional PageRank algorithm, the idea of inverse PageRank is that a node is crucial if it directs to other important nodes. Subsequently, the fault propagation coefficients of components in the quantitative causal diagram are derived and the fault propagation path is identified. The proposed method is validated on the computer numerically controlled machine tool, and the results are basically consistent with the typical cascading events and conform to expert judgment.

Automated summary

Computer numerically controlled machine tools are crucial in the intelligent manufacturing industry, and unscheduled shutdowns can cause equipment damage and production loss. This paper proposes a novel fault propagation analysis method that accurately evaluates fault propagation coefficients of components in these machine tools. By building a quantitative causal diagram and using an inverse PageRank algorithm, the fault propagation risks of components are assessed, and the fault propagation path is identified. The method is validated on a computer numerically controlled machine tool, and the results match typical cascading events and expert judgment.