This　brief　addresses　the　data-driven　fault　monitoring　issues　for　nonlinear　systems　with　dynamic　operations　by　taking　into　account　the　key　performance　indicators　(KPIs).　To　reach　this　objective,　the　KPIs　related　to　the　other　variables　of　the　nonlinear　systems　are　first　established　by　means　of　the　Takagi-Sugeno　fuzzy　technique.　Then,　the　input/output　data　model　of　the　KPIs　is　constructed　in　fuzzy　form.　By　applying　the　subspace-aided　method,　a　fuzzy-model-based　KPI　predictor　is　obtained.　On　this　basis,　a　data-driven　realization　algorithm　of　the　KPI　prediction　residual　generator　is　proposed　with　available　system　measurements.　Towards　KPI-related　fault　monitoring　purpose,　the　control　limit　is　computed　by　utilizing　the　kernel　density　estimation　method,　with　which　an　online　KPI-related　monitoring　scheme　for　dynamic　nonlinear　systems　is　presented.　Compared　with　the　existing　methods,　the　merits　of　this　scheme　lie　in　that　it　can　handle　the　nonlinear　and　dynamic　characteristics　of　data,　so　as　to　improve　the　detection　performance　of　KPI-related　faults.　Meanwhile,　the　KPI　prediction　model　can　achieve　interpretability　by　introducing　the　fuzzy　modeling.　An　experimental　study　on　the　ship　propulsion　system　is　finally　given　to　demonstrate　the　developed　results.