The　focus　on　process　safety　and　product　quality　has　propelled　the　adoption　of　sophisticated　data-driven　methodologies,　with　multivariate　statistical　process　monitoring　(MSPM)　becoming　a　cornerstone　in　process　industries.　However,　as　these　industries　expand　and　generate　increasingly　complex　datasets,　conventional　MSPM　frameworks　often　fail　to　capture　intricate　process　dynamics,　complicating　fault　detection　and　diagnosis.　Additionally,　most　MSPM　approaches　do　not　consider　the　moderating　influence　of　closed-loop　controllers　on　abnormal　conditions,　frequently　leading　to　misclassification　of　operational　transitions　as　faults.　To　address　these　challenges,　this　paper　presents　a　Multi-Subspace　Quality-Aware　Slow　Feature　Analysis　(MQASFA)　method　for　concurrently　monitoring　operational　deviations　and　anomalous　behavior.　The　MQASFA　framework　employs　a　multi-subspace　strategy　using　symmetric　Kullback-Leibler　divergence　to　aggregate　correlated　variables　from　a　probabilistic　standpoint.　A　divisive　hierarchical　clustering　algorithm　is　applied　to　integrate　variable　blocks,　reducing　computational　complexity　and　redundancy　while　preserving　essential　local　process　information.　A　quality-aware　slow　feature　analysis　submodel　is　subsequently　deployed　for　decentralized,　quality-focused　monitoring　within　each　subspace.　To　differentiate　routine　operational　variations　from　significant　anomalies,　novel　static　and　dynamic　metrics　derived　from　Support　Vector　Data　Description　are　introduced.　The　efficacy　of　the　proposed　methodology　is　validated　through　applications　to　the　Tennessee　Eastman　process　and　a　wastewater　treatment　benchmark.