AIChE Journal · 2015 · 143 citations · 36 references
EngineeringMachine LearningEducationMeasurement ModelingStochastic AnalysisSoft SensorLatent ModelingQuality VariablesData SciencePattern RecognitionStochastic ProcessesSystems EngineeringStatisticsSlow FeaturesLatent Variable MethodsProcess MeasurementFeature EngineeringPredictive AnalyticsProcess MonitoringProcess AnalysisLatent VariableComputer ScienceProcess Systems EngineeringFeature ConstructionProbabilistic Slow FeatureStochastic ModelingProcess ControlMassive Process DataSoft Sensor Modeling
Latent variable models explicitly represent underlying driving forces of process variations and retain the dominant information of process data. The study derives slow features as temporally correlated latent variables via probabilistic SF analysis and proposes an efficient EM algorithm to estimate model parameters for massive process data. Probabilistic SF analysis is used to derive slow features, an efficient EM algorithm estimates the model parameters, and two criteria are introduced to select quality‑relevant SFs. State‑space slow features capture nominal process variations correlated with quality variables, improving soft‑sensor prediction, and the method’s validity and advantages are demonstrated in two case studies. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4126–4139.
Latent variable (LV) models provide explicit representations of underlying driving forces of process variations and retain the dominant information of process data. In this study, slow features (SFs) as temporally correlated LVs are derived using probabilistic SF analysis. SFs evolving in a state‐space form effectively represent nominal variations of processes, some of which are potentially correlated to quality variables and hence help improving the prediction performance of soft sensors. An efficient expectation maximum algorithm is proposed to estimate parameters of the probabilistic model, which turns out to be suitable for analyzing massive process data. Two criteria are also proposed to select quality‐relevant SFs. The validity and advantages of the proposed method are demonstrated via two case studies. © 2015 American Institute of Chemical Engineers AIChE J , 61: 4126–4139, 2015
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