In　the　wastewater　treatment　process　(WWTP),　it　is　crucial　to　monitor　the　state　of　the　equipment　for　maintaining　high-quality　automation.　When　the　equipment　are　subject　to　multidimensional　states,　they　always,　however,　suffer　from　cross-interference　among　available　state　indicators　due　to　their　unclear　causal　relationship.　To　address　this　issue,　in　this　work,　a　multidimensional　state　predictive　model　(MDSP),　based　on　the　cascaded　long-short-term　memory　(Cd-LSTM),　is　developed　to　monitor　the　states　of　equipment.　First,　constructing　with　Cd-LSTM　for　predicting　multidimensional　state　indicators,　the　proposed　MDSP　is　able　to　obtain　causal　relationships　among　indicators.　Second,　several　dynamic　data　storage　units　embedded　into　Cd-LSTM　are　developed　to　regulate　the　associative　strength　between　indicators　based　on　the　acquired　causal　relationship,　which　can　alleviate　cross-interference.　Third,　the　independent　parameters　and　shared　parameters　of　Cd-LSTM　are　updated　by　the　algorithm　of　joint　backpropagation　gradients　to　maintain　a　reliable　prediction.　Finally,　to　verify　the　proposed　MDSP,　some　experiments　are　carried　out　on　a　real-world　blower　dataset　in　WWTP　to　predict　the　blower　states.　The　experimental　results　confirmed　that　the　proposed　strategy　has　performed　excellently.