Learning Sparse Kernel CCA With Graph Priors for Nonlinear Process Monitoring

Gap-MK-DCCA-Based Intelligent Fault Diagnosis for Nonlinear Dynamic Systems

“…Reactor pressure kPa XMEAS (8) Reactor level % XMEAS (9) Reactor temperature • C XMEAS (10) Discharge rate (stream 9) km 3 /h XMEAS (11) Product separator temperature • C XMEAS (12) Product separator level % XMEAS (13) Product separator pressure kPa XMEAS (14) Product separator bottom flow rate (stream 10) m 3 /h XMEAS (15) Stripper tower level % XMEAS (16) Stripper tower level pressure kPa XMEAS (17) Stripper tower bottom flow rate (stream 11) m 3 /h XMEAS (18) Stripper tower temperature • C XMEAS (19) Stripper tower flow rate kg/h XMEAS (20) Compressor power kW XMEAS (21) Reactor cooling water outlet temperature • C XMEAS (22) Separator cooling water outlet temperature • C XMEAS (23) Component A (stream 6) mol% XMEAS (24) Component B (stream 6) mol% XMEAS (25) Component C (stream 6) mol% XMEAS (26) Component D (stream 6) mol% XMEAS (27) Component E (stream 6) mol% XMEAS (28) Component F (stream 6) mol% XMEAS (29) Component A (stream 9) mol% XMEAS (30) Component B (stream 9) mol% XMEAS (31) Component C (stream 9) mol% XMEAS (32) Component D (stream 9) mol% XMEAS (33) Component E (stream 9) mol% XMEAS (34) Component F (stream 9) mol% XMEAS (35) Component G (stream 9) mol% XMEAS (36) Component H (stream 9) mol% XMEAS (37) Component…”

“…The majority of traditional process monitoring methods are rooted in multivariate statistical analysis techniques, including Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Variate Analysis (CVA), Canonical Variate Dissimilarity Analysis (CVDA), and Canonical Correlation Analysis (CCA), which have been extensively researched by a wide range of scholars and have yielded noteworthy research achievements and a plethora of successful applications [1]. For instance, references [2][3][4][5][6][7] have, respectively, investigated and improved the PCA method to varying extents and achieved successful applications in the industry; Ding et al applied an enhanced PLS to predict and diagnose key performance indicators of industrial hot-rolled strip steel mills [8], and a series of researches were similarly carried out for the PLS method in [9][10][11][12][13]; Ruiz-Cárcel and colleagues achieved satisfactory experimental results in the process monitoring of multiphase flow facilities using the CVA method [14]; subsequently, Pilario et al proposed the CVDA method and its extended version based on CVA, offering new insights into incipient fault detection in dynamic systems [15][16][17]; as for the CCA method, Chen et al pioneered the use of data-driven CCA technology for generating residuals based on canonical correlation, applying it to fault detection in both static and dynamic processes [18]; following this, the CCA method has gradually attracted attention from scholars in the field of process monitoring and fault detection, undergoing extensive research and improvement [19][20][21][22][23][24][25][26][27][28].…”