In　complex　industrial　environments,　the　accuracy　of　sensing　data　interpretation　directly　affects　the　efficiency,　reliability,　and　safety　of　system　operation.　However,　traditional　modeling　approaches　often　struggle　to　handle　the　multi-dimensional,　noisy,　and　highly　dynamic　characteristics　of　industrial　sensing　data.　This　study　provides　different　modeling　frameworks　for　parsing　sensing　data　in　complex　industrial　scenarios,　and　the　developed　models　demonstrate　significant　engineering　application　value　in　terms　of　improving　prediction　accuracy　and　reducing　false　alarm　rate.　In　this　study,　a　ensemble　model　was　proposed,　different　models　are　used　for　comparison,　and　the　classification　performance　of　four　types　of　algorithms,　namely　K-Nearest　Neighbors　(KNN),　Artificial　Neural　Network　(ANN),　Random　Forest　(RF)　and　Gradient　Boosting　Decision　Tree　(GBDT),　is　systematically　compared　and　analyzed　through　the　importance　feature　analysis,　and　then　their　weights　are　computed　and　fused,　and　the　best　two　models,　RF　and　GBDT,　are　finally　selected.　In　this　article,　the　industrial　sensing　dataset　is　standardized　by　Kaggle　platform,　and　feature　orthogonal　coding　and　preprocessing　processes　are　used　to　construct　a　diagnostic　feature　space　containing　multi-dimensional　features　such　as　temperature　field,　rotor　speed　and　three-axis　vibration.　Experiments　show　that　the　comprehensive　performance　index　of　the　ensemble　model　(F1=0.2637)　is　substantially　improved　compared　with　the　optimal　single　model,　and　temperature,　rotational　speed　(RPM),　fuel　efficiency,　three-axis　vibrations　(X,　Y,　Z),　torque,　and　power　output　are　verified　as　the　key　fault-sensitive　parameters　through　the　quantization　of　feature　importance.　This　article　proves　the　predictive　validity　of　the　ensemble　model　in　this　task.　©　2025　IEEE.