Coupling　relations　analysis　of　monitoring　variables　in　complex　electromechanical　system　is　a　powerful　and　useful　means　for　abnormal　detection,　false　monitoring　information　identification　and　fault　diagnosis.　However,　due　to　the　characteristics　of　multivariable,　nonlinear　and　non-stationarity　in　the　actual　production　process,　it　is　difficult　to　achieve　multivariable　coupling　modeling　of　complex　electromechanical　systems.　In　this　paper,　a　new　coupling　modeling　method　is　proposed　by　combining　causality　analysis　with　RBF　neural　network.　First,　considering　the　multivariate　and　nonlinearity　of　complex　system,　the　conditional　Granger　and　nonlinear　Granger　is　combined　to　analyze　the　causal　relations　of　process　variables　and　obtain　the　cause　variable　set　of　any　variable　in　complex　system.　Second,　the　RBF　neural　network　is　applied　to　achieve　the　nonlinear　fitting　and　the　parameter　is　optimized　to　ensure　the　fitting　precision.　Finally,　the　effectiveness　of　the　proposed　method　is　verified　by　an　analysis　of　one　case　study　of　real　compressor　groups　data　set　in　chemical　production　system.　This　new　approach　can　handle　general　coupling　modeling　problems　and　obtain　a　quantitative　nonlinear　coupling　model　by　determine　the　dependent　and　independent　variables　in　system　and　the　functional　relationship　between　them.　Which　dedicated　to　studying　quantitative　functional　relationship　of　variable　coupling,　not　just　considering　the　direction　or　strength　of　coupling　as　in　the　existing,　and　does　not　need　any　prior　knowledge　about　the　physical　structure.　Thus,　the　proposed　method　can　be　effectively　used　in　coupling　modeling　of　complex　electromechanical　systems　and　formulate　the　foundation　of　anomaly　detection,　information　quality　assessment,　and　failure　propagation　mechanism,　as　well　as　other　engineering　applications.　©　2020