@article{scholars1313,
           title = {A fault detection and diagnosis strategy for batch/semi-batch processes},
         journal = {Chemical Product and Process Modeling},
             doi = {10.2202/1934-2659.1440},
            year = {2010},
            note = {cited By 3},
          volume = {5},
          number = {1},
        abstract = {This paper presents fault detection and diagnosis methodology for batch/semi-batch processes using a multi-way orthogonal nonlinear PCA approach. In this work, a sequential extracting process of linear and nonlinear correlations from process data is performed. The approach reduces the complexity of the nonlinear PCA model structure, which dramatically improves the model generalization. An orthogonal nonlinear PCA procedure is incorporated to capture the nonlinear characteristics with a minimum number of principal components. A trajectory-boundary-limit crossing point discriminant analysis is proposed to diagnose the process faults. A two-step discriminant analysis is also incorporated to improve the diagnostic performance in the case of isotropically distributed trajectories. The validity of the proposed strategy is demonstrated by the application to an emulsion copolymerization of styrene/MMA semi-batch process. {\^A}{\copyright} 2010 Berkeley Electronic Press. All rights reserved.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952476389&doi=10.2202\%2f1934-2659.1440&partnerID=40&md5=8382bb5d5d5466b828d7b566cabdcfaa},
        keywords = {Batch process; Crossing point; Diagnostic performance; Emulsion copolymerization; Fault detection and diagnosis; Model generalization; Non-linear correlations; Nonlinear characteristics; Nonlinear PCA; Principal Components; Process data; Process faults; Semi-batch process; semi-batch processes, Batch data processing; Discriminant analysis; Emulsification; Neural networks; Principal component analysis; Process control; Process monitoring, Fault detection},
          author = {Maulud, A. and Romagnoli, J.},
            issn = {19342659}
}