The　accuracy　maintenance　and　operation　reliability　of　the　hydrostatic　turntable　are　associated　with　many　factors　whose　influence　on　bearing　load　carrying　capacity　is　the　key　question　of　researching　the　hydrostatic　turntable.　In　this　study,　an　intelligent　hydrostatic　turntable　monitoring　system　is　proposed　based　on　multi-source　data　acquisition.　Firstly,　the　timely　main　parameter　variations　are　acquired　through　different　kinds　of　sensors　installed　on　the　turntable　to　form　an　input　data　aggregation.　Then,　by　analyzing　the　static,　dynamic,　and　thermal　characteristics　of　the　turntable　using　Finite　Difference　Method　(FDM)　and　Runge–Kutta　method　(RKM),　a　set　of　equations　for　the　bearing　performance　evaluation　is　established.　Computational　results　are　converted　to　the　motor　signal　for　the　pump　for　controlling　oil　supply　rate.　Finally,　the　load-carry　supporting　performance　of　each　oil　pad　is　controlled　precisely　and　individually　by　a　pump　with　multiple　outputs　driven　by　servo　motors.　An　intelligent　input,　processing,　and　output　(IPO)　real-time　system　of　the　hydrostatic　turntable　is　established　to　monitor　its　service　performance.　Finally,　the　accuracy　compensation　by　data　statistics　and　reliability　evaluation　by　abnormal　data　detection　are　carried　out　to　improve　the　working　performance　of　hydrostatic　turntable.　©　2018,　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.