Automatic identification of causal knowledge and causal graphs in technical systems of process ventilators

摘要

This research paper presents the approach of automated computerized identification of causal knowledge and causal graphs using monitoring of vibrations and temperatures of sliding bearings of high-power and high-speed process ventilators. Method of Granger causal connectivity analysis of vibration and temperature parameters is presented. This method improves diagnostics of process ventilators because of identification of causal relations and links of vibrations and temperatures in graph form. After computing and plotting causal graphs for vibrations and temperatures, causal density is computed as a measure of dynamical complexity of system. Numerical values of causal density are taken as indicators of systems "health" of process ventilators.