Add advanced reconstruction models

[afraniomelo]

How we are today

Reconstruction models aim to represent normal process behavior by utilizing a mathematical structure that incorporates all relevant variables simultaneously. The objective is typically to create a compressed representation of the data that still captures the essential information, often involving dimensionality reduction methods. During the test phase, the data is compared to the identified structure to detect any deviations. For more details, please check MELO et al. (2024) [1].

Currently, the following reconstruction models are implemented in BibMon:

• Principal Component Analysis (PCA);
• Autoencoders (AE);
• Similarity-based method (SBM).

Proposed enhancement

There is extensive literature on reconstruction methodologies for multivariate statistical process monitoring. Some examples include:

• Advanced versions of PCA:
  • Multi-Way PCA (mPCA) [2], [3] for batch processes;
  • Dynamic-Inner PCA (DiPCA) [4], [5] or Recursive PCA (RPCA) [6] for dynamic processes;
  • Multiscale PCA (MSPCA) [7] for analyzing multiple time scales;
  • Kernel PCA (kPCA) [8] for nonlinear processes;
• Partial Least Squares (PLS) [9], [10];
• Canonical Correlation Analysis (CCA) [11], [10];
• Fisher Discriminant Analysis (FDA) [12];
• Independent Component Analysis (ICA) [13], [14];
• Slow Feature Analysis (SFA) [15], [16].

For a comprehensive review and analysis of these techniques, please refer to MELO et al. (2024) [17].

Implementation

New reconstruction models can be implemented by inheriting from GenericModel class. For more instructions, please refer to the contributing guide.

References

[1] Melo, A., Lemos, T. S., Soares, R. M., Spina, D., Clavijo, N., Campos, L. F. D. O., ... & Pinto, J. C. (2024). BibMon: An open source Python package for process monitoring, soft sensing, and fault diagnosis. Digital Chemical Engineering, 100182.

[2] Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361-1375.

[3] Rendall, R., Chiang, L. H., & Reis, M. S. (2019). Data-driven methods for batch data analysis–A critical overview and mapping on the complexity scale. Computers & Chemical Engineering, 124, 1-13.

[4] Dong, Y., & Qin, S. J. (2018). A novel dynamic PCA algorithm for dynamic data modeling and process monitoring. Journal of Process Control, 67, 1-11.

[5] Dong, Y., & Qin, S. J. (2018). Dynamic latent variable analytics for process operations and control. Computers & Chemical Engineering, 114, 69-80.

[6] Li, W., Yue, H. H., Valle-Cervantes, S., & Qin, S. J. (2000). Recursive PCA for adaptive process monitoring. Journal of Process Control, 10(5), 471-486.

[7] Bakshi, B. R. (1998). Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal, 44(7), 1596-1610.

[8] Lee, J. M., Yoo, C., Choi, S. W., Vanrolleghem, P. A., & Lee, I. B. (2004). Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59(1), 223-234.

[9] Rosipal, R., & Krämer, N. (2005, February). Overview and recent advances in partial least squares. In International Statistical and Optimization Perspectives Workshop" Subspace, Latent Structure and Feature Selection" (pp. 34-51). Berlin, Heidelberg: Springer Berlin Heidelberg.

[10] Zhang, K., Peng, K., Chu, R., & Dong, J. (2018). Implementing multivariate statistics-based process monitoring: A comparison of basic data modeling approaches. Neurocomputing, 290, 172-184.

[11] Yang, X., Liu, W., Liu, W., & Tao, D. (2019). A survey on canonical correlation analysis. IEEE Transactions on Knowledge and Data Engineering, 33(6), 2349-2368.

[12] Chiang, L. H., Russell, E. L., & Braatz, R. D. (2000). Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometrics and Intelligent Laboratory Systems, 50(2), 243-252.

[13] Lee, J. M., Yoo, C., & Lee, I. B. (2004). Statistical process monitoring with independent component analysis. Journal of Process Control, 14(5), 467-485.

[14] Palla, G. L. P., & Pani, A. K. (2023). Independent component analysis application for fault detection in process industries: Literature review and an application case study for fault detection in multiphase flow systems. Measurement, 209, 112504.

[15] Shang, C., Yang, F., Gao, X., Huang, X., Suykens, J. A., & Huang, D. (2015). Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE Journal, 61(11), 3666-3682.

[16] Song, P., & Zhao, C. (2022). Slow down to go better: A survey on slow feature analysis. IEEE Transactions on Neural Networks and Learning Systems, 35(3), 3416-3436.

[17] Melo, A., Câmara, M. M., & Pinto, J. C. (2024). Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey. Processes, 12(2), 251.