Adaptive Inferential Sensors Based on Evolving Fuzzy Models

A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the challenges of the modern advanced process industry.

[1]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[2]  Plamen P. Angelov,et al.  Soft sensor for predicting crude oil distillation side streams using evolving takagi-sugeno fuzzy models , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[3]  Plamen Angelov,et al.  Identification of Evolving Rule-based Models. , 2002 .

[4]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[5]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Andrés Ferreyra,et al.  A new on-line self-constructing neural fuzzy network , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[7]  Thorsten Joachims,et al.  Detecting Concept Drift with Support Vector Machines , 2000, ICML.

[8]  Arthur K. Kordon,et al.  Soft sensor development using genetic programming , 2001 .

[9]  S. M. Alexander,et al.  Monitoring, diagnosis and control of industrial processes , 1998 .

[10]  P. Angelov,et al.  Evolving Fuzzy Systems from Data Streams in Real-Time , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[11]  Paul C. Rhodes,et al.  Essentials of Fuzzy Modelling and Control , 1995 .

[12]  Jialin Liu,et al.  On-line soft sensor for polyethylene process with multiple production grades , 2007 .

[13]  Frank Klawonn,et al.  Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[14]  Plamen P. Angelov,et al.  Identification of evolving fuzzy rule-based models , 2002, IEEE Trans. Fuzzy Syst..

[15]  Plamen Angelov,et al.  Evolving Rule-Based Models: A Tool For Design Of Flexible Adaptive Systems , 2002 .

[16]  Arthur K. Kordon,et al.  Robust soft sensor development using genetic programming , 2003 .

[17]  Ronald R. Yager,et al.  Essentials of fuzzy modeling and control , 1994 .

[18]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[19]  Lúcia Valéria Ramos de Arruda,et al.  Soft sensor based on fuzzy model identification , 2005 .

[20]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[22]  Plamen Angelov,et al.  An Approach to Autonomous Self-localization of a Mobile Robot in Completely Unknown Environment using Evolving Fuzzy Rule-based Classifier , 2007 .

[23]  Plamen Angelov,et al.  Intelligent interrogation of mid-IR spectroscopy data from exfoliative cervical cytology using self-learning classifier eClass , 2008 .

[24]  Alex Arenas,et al.  Neural virtual sensor for the inferential prediction of product quality from process variables , 2002 .

[25]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[26]  R. French.  Catastrophic Forgetting in Connectionist Networks , 2006 .

[27]  Xiao Fan Wang,et al.  Soft sensing modeling based on support vector machine and Bayesian model selection , 2004, Comput. Chem. Eng..

[28]  Jialin Liu.  On-line soft sensor for polyethylene process with multiple production grades , 2005 .

[29]  Geoff Hulten,et al.  Catching up with the Data: Research Issues in Mining Data Streams , 2001, DMKD.

[30]  Plamen P. Angelov,et al.  On line learning fuzzy rule-based system structure from data streams , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[31]  Eduardo Gómez-Sánchez,et al.  Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems , 2004 .

[32]  Plamen P. Angelov,et al.  Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[33]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[34]  A. L. Dexter,et al.  A fuzzy sensor for measuring the mixed air temperature in air-handling units , 2005 .