Slow　Feature　Analysis　(SFA)　has　gained　prominence　in　process　monitoring　due　to　its　capability　to　capture　inertial　features　in　industrial　systems.　However,　traditional　SFA　methods　are　predominantly　unsupervised　and　often　neglect　output　quality,　limiting　their　effectiveness　in　large-scale,　complex　systems.　To　address　these　limitations,　this　paper　introduces　the　Ensemble　Quality-Aware　Slow　Feature　Analysis　(EQASFA)　framework,　which　maximizes　the　correlation　between　quality　variables　and　slow　features.　This　decentralized　monitoring　framework　generates　fine-grained　submodels　by:　(i)　constructing　a　diverse　set　of　submodels　through　different　variable　combinations,　and　(ii)　selecting　base　submodels　with　the　lowest　false　alarm　rate　on　the　validation　dataset.　The　selection　process　utilizes　a　divisive　hierarchical　clustering　algorithm,　where　probabilistic　similarity　is　quantified　using　symmetric　Kullback-Leibler　divergence.　In　addition,　novel　static　and　dynamic　metrics,　derived　from　Bayesian　inference,　are　proposed　to　distinguish　routine　operational　fluctuations　from　significant　anomalies.　The　performance　of　the　EQASFA　framework　is　validated　through　two　benchmark　case　studies:　the　Tennessee　Eastman　process　and　a　wastewater　treatment　process.