JACIII Vol.28 No.2 pp. 284-295
doi: 10.20965/jaciii.2024.p0284

Research Paper:

ILFDA Model: An Online Soft Measurement Method Using Improved Local Fisher Discriminant Analysis

Jian Peng*,**,***, Liangcheng Zhao*,**,***,†, Yilun Gao*,**,***, and Jianjun Yang*,**,***

*School of Automation, China University of Geosciences
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

***Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

Corresponding author

March 16, 2023
October 20, 2023
March 20, 2024
soft measurement, JITL, similarity measure, ILFDA

With the advancement of soft measurement, just-in-time learning (JITL) has become a widely adopted framework for online soft-sensing modeling in industrial processes. However, traditional JITL model approaches often rely on simple similarity measures like Euclidean distance, resulting in the underutilization of labeled data. This paper proposes a supervised, improved local Fisher discriminant analysis method based on a JITL framework and local Fisher discriminant analysis (LFDA) to improve data utilization efficiency. In particular, by incorporating the indirect correlation information matrix, this method integrates the inter-class and intra-class dispersion matrix, overcoming the limitation of the LFDA algorithm that only captures direct data correlations. We select two different carbon depositions in the Methanol-to-Olefin reaction system for comparative experiments and use the root mean squared error (RMSE) and R-square (R2) to evaluate the effectiveness of the proposed method. Fitting results show that two kinds of carbon depositions were better than the control model, namely the RMSE of the model were 0.1431 and 0.1513, R2 were 0.8952 and 0.8707.

Cite this article as:
J. Peng, L. Zhao, Y. Gao, and J. Yang, “ILFDA Model: An Online Soft Measurement Method Using Improved Local Fisher Discriminant Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.2, pp. 284-295, 2024.
Data files:
  1. [1] S. Imori and H. Shimodaira, “An Information Criterion for Auxiliary Variable Selection in Incomplete Data Analysis,” Entropy, Vol.21, No.3, 2019.
  2. [2] Z. Lou and Y. Wang, “New Nonlinear Approach for Process Monitoring: Neural Component Analysis,” Industrial & Engineering Chemistry Research, Vol.60, No.1, pp. 387-398, 2020.
  3. [3] Q. Sun and Z. Ge, “A Survey on Deep Learning for Data-Driven Soft Sensors,” IEEE Trans. on Industrial Informatics, Vol.17, No.9, pp. 5853-5866, 2021.
  4. [4] Z. Ge and Z. Song, “A Comparative Study of Just-in-Time-Learning Based Methods for Online Soft Sensor Modeling,” Chemometrics and Intelligent Laboratory Systems, Vol.104, No.2, pp. 306-317, 2010.
  5. [5] Y. Bai and M. Bain, “Optimizing Weighted Lazy Learning and Naive Bayes Classification Using Differential Evolution Algorithm,” J. of Ambient Intelligence and Humanized Computing, Vol.13, pp. 3005-3024, 2022.
  6. [6] A. Talamantes and E. Chavez, “Instance-Based Learning Using the Half-Space Proximal Graph,” Pattern Recognition Letters, Vol.156, pp. 88-95, 2022.
  7. [7] X. Jiang and Z. Ge, “Improving the Performance of Just-in-Time Learning-Based Soft Sensor Through Data Augmentation,” IEEE Trans. on Industrial Electronics, Vol.69, No.12, pp. 13716-13726, 2022.
  8. [8] Y. Gao, H. Jin, B. Wang, B. Yang, and W. Yu, “An Adaptive Soft Sensor Method Based on Online Deep Evolving Fuzzy System for Industrial Process Data Streams,” 2023 IEEE 12th Data Driven Control and Learning Systems Conf. (DDCLS), pp. 1799-1804, 2023.
  9. [9] P. Zhou, W. Chen, C. Yi, Z. Jiang, T. Yang, and T. Chai, “Fast Just-in-Time-learning Recursive Multi-Output LSSVR for Quality Prediction and Control of Multivariable Dynamic Systems,” Engineering Applications of Artificial Intelligence, Vol.100, Article No.104168, 2021.
  10. [10] S. Dong, Y. Li, P. Zhu, X. Pei, X. Pan, X. Xu, L. Liu, B. Xing, and X. Hu, “Rolling bearing performance degradation assessment based on singular value decomposition-sliding window linear regression and improved deep learning network in noisy environment,” Measurement Science and Technology, Vol.33, No.4, Article No.045015, 2022.
  11. [11] D. Aguado, G. Noriega-Hevia, J. Ferrer, A. Seco, and J. Serralta, “PLS-Based Soft-Sensor to Predict Ammonium Concentration Evolution in Hollow Fibre Membrane Contactors for Nitrogen Recovery,” J. of Water Process Engineering, Vol.47, Article No.102735, 2022.
  12. [12] X. Huo, K. Hao, L. Chen, X.-S. Tang, T. Wang, and X. Cai, “A Dynamic Soft Sensor of Industrial Fuzzy Time Series with Propositional Linear Temporal Logic,” Expert Systems with Applications, Vol.201, Article No.117176, 2022.
  13. [13] J. C. Gower, “Properties of Euclidean and non-Euclidean distance matrices,” Linear Algebra and its Applications, Vol.67, pp. 81-97, 1985.
  14. [14] G. Verdier and A. Ferreira, “Adaptive MD and k-Nearest Neighbor Rule for Fault Detection in Semiconductor Manufacturing,” IEEE Trans. on Semiconductor Manufacturing, Vol.24, No.1, pp. 59-68, 2010.
  15. [15] C. Cheng and M. S. Chiu, “A New Data-Based Methodology for Nonlinear Process Modeling,” Chemical Engineering Science, Vol.59, No.13, pp. 2801-2810, 2004.
  16. [16] Q. Guo, P. Xu, H. Wang, and J. Liu, “Multimode Process Monitoring Strategy Based on Improved Just-in-Time-Learning Associated with Locality Preserving Projections,” The Canadian J. of Chemical Engineering, Vol.101, No.4, pp. 2002-2017, 2023.
  17. [17] G. Fan, X. Ruimin, and H. Biao, “A Deep Learning Just-in-Time Modeling Approach for Soft Sensor Based on Variational Autoencoder,” Chemometrics and Intelligent Laboratory Systems, Vol.197, Article No.103922, 2020.
  18. [18] J. Zheng, F. Shen, and L. Ye, “Improved MD Based JITL-LSTM Soft Sensor for Multiphase Batch Processes,” IEEE Access, Vol.9, pp. 72172-72182, 2021.
  19. [19] M. Sugiyama, “Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis,” J. of Machine Learning Research, Vol.8, No.5, pp. 1027-1061, 2007.
  20. [20] P. Xanthopoulos, P. M. Pardalos, and T. B. Trafalis, “Linear Discriminant Analysis,” Robust Data Mining, pp. 27-33, 2013.
  21. [21] T. Yan, D. Wang, T. Xia, J. Liu, Z. Peng, and L. Xi, “Investigation on Optimal Discriminant Directions of Linear Discriminant Analysis for Locating Informative Frequency Bands for Machine Health Monitoring,” Mechanical Systems and Signal Processing, Vol.180, Article No.109424, 2022.
  22. [22] J. Wang, H. Jiang, and Q. Chen, “High-Precision Recognition of Wheat Mildew Degree Based on Colorimetric Sensor Technique Combined with Multivariate Analysis,” Microchemical J., Vol.168, Article No.106468, 2021.
  23. [23] R. Ran, Y. Ren, S. Zhang, and B. Fang, “A Novel Discriminant Locality Preserving Projections Method,” J. of Mathematical Imaging and Vision, Vol.63, pp. 541-554, 2021.
  24. [24] X. Zhu, S. K. Damarla, K. Hao, and B. Huang “Parallel Interaction Spatiotemporal Constrained Variational Autoencoder for Soft Sensor Modeling,” IEEE Trans. on Industrial Informatics, Vol.18, No.8, pp. 5190-5198, 2021.
  25. [25] M. Yang, D. Fan, Y. Wei, P. Tian, and Z. Liu, “Recent Progress in Methanol-to-Olefins (MTO) Catalysts,” Advanced Materials, Vol.31, No.50, Article No.1902181, 2019.
  26. [26] P. Chen and Y. Lu, “Extremal Optimization for Optimizing Kernel Function and Its Parameters in Support Vector Regression,” J. of Zhejiang University (Science C), Vol.12, No.4, pp. 297-306, 2011.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 05, 2024