An Integrated Approach for Process Control Valves Diagnosis Using Fuzzy Logic
Affiliation(s)
Nuclear Energy Research Institute/National Nuclear Energy Commission (IPEN/CNEN—SP), S?o Paulo, Brasil . 2 Navy Technology Center/Navy Ministry (CTMSP/MB), S?o Paulo, Brasil.
Nuclear Energy Research Institute/National Nuclear Energy Commission (IPEN/CNEN—SP), S?o Paulo, Brasil . 2 Navy Technology Center/Navy Ministry (CTMSP/MB), S?o Paulo, Brasil.
ABSTRACT
Control valves are widely
used in industry to control fluid flow in several applications. In nuclear
power systems they are crucial for the safe operation of plants. Therefore, the
necessity of improvements in monitoring and diagnosis methods started to be of
extreme relevance, establishing as main goal of the reliability and readiness
of the system components. The main focus of this work is to study the
development of a model of non-intrusive monitoring and diagnosis applied to process
control valves using artificial intelligence by fuzzy logic technique,
contributing to the development of predictive methodologies identifying faults in
incipient state. Specially in nuclear power plants, the predictive maintenance
contributes to the security factor in order to diagnose in advance the
occurrence of a possible failure, preventing severs situations. The control
valve analyzed belongs to a steam plant which simulates the secondary circuit of
a PWR—Pressurized Water Reactor. The maintenance programs are being implemented
based on the ability to diagnose modes of degradation and to take measures to
prevent incipient failures, improving plant reliability and reducing
maintenance costs. The approach described in this paper represents an
alternative departure from the conventional qualitative techniques of system
analysis. The methodology used in this project is based on signatures analysis,
considering the pressure (psi) in the actuator and the stem displacement (mm)
of the valve. Once the measurements baseline of the control valve is taken, it
is possible to detect long-term deviations during valve lifetime, detecting in
advance valve failures. This study makes use of MATLAB language through the “fuzzy
logic toolbox” which uses the method of inference “Mamdani”, acting by fuzzy conjunction,
through Triangular Norms (t-norm) and Triangular Conorms (t-conorm). The main
goal is to obtain more detailed information contained in the measured data,
correlating them to failure situations in the incipient stage.
Cite this paper
Carneiro, A. and Porto Jr., A. (2014) An Integrated Approach for Process Control Valves Diagnosis Using Fuzzy Logic. World Journal of Nuclear Science and Technology, 4, 148-157. doi: 10.4236/wjnst.2014.43019.
Carneiro, A. and Porto Jr., A. (2014) An Integrated Approach for Process Control Valves Diagnosis Using Fuzzy Logic. World Journal of Nuclear Science and Technology, 4, 148-157. doi: 10.4236/wjnst.2014.43019.
References
[1] Coble, J.B. and Upadhyaya, B.R. (2012) Prognostic and Health Management in Nuclear Power Plants: A Review of Technologies and Applications. National Technical Information Service. [2] Guimaraes Carneiro, A.L. (2003) Development of an Integrated Condition Monitoring and Diagnostic System for Motor-Operated Valves used in Nuclear Power Plant. Ph.D. Thesis, IPEN-USP, Sao Paulo, 22710. (in Portuguese). [3] Pinto, C. and M. de Almeida, G. (2010) Failure Detection in a Control Final Element via Markov Model. Regional Meeting of Computational Applied Mathmatics—ERMAC, UFSJ, Ouro Branco. (in Portuguese). [4] Guimaraes Carneiro, A.L. and Porto Jr., A.C.S. (2013) Development of an Integrated Condition Monitoring and Diagnostic System for Process Control Valves Used in Nuclear Power Plant. AIDIC PHM, Milano, 872. [5] Attaway, S. (2009) MATLAB A Practical Introduction to Programming and Problem Solving. Elsevier, Amsterdam. [6] Zadeh, L.A. (1984) Coping with the Imprecision of the Real World: An Interview with Lofti Zadeh. Communications of the ACM, 27, 304-311.
http://dx.doi.org/10.1145/358027.358032 [7] Mendel, J.M. (1995) Fuzzy Logic Systems for Engineering: A Tutorial. Proceedings of the IEEE, 83, 83. [8] Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. [9] Ross, T.J. (2010) Fuzzy Logic with Engineering Applications. 3rd Edition, Wiley, Hoboken.
http://dx.doi.org/10.1002/9781119994374 [10] Mamdani, E.H. (1974) Application of Fuzzy Logic to Approximate Reasoning using Linguistic Synthesis. Department of Electrical and Electronic Engineering, Queen Mary College (University of London), London.
[1] Coble, J.B. and Upadhyaya, B.R. (2012) Prognostic and Health Management in Nuclear Power Plants: A Review of Technologies and Applications. National Technical Information Service. [2] Guimaraes Carneiro, A.L. (2003) Development of an Integrated Condition Monitoring and Diagnostic System for Motor-Operated Valves used in Nuclear Power Plant. Ph.D. Thesis, IPEN-USP, Sao Paulo, 22710. (in Portuguese). [3] Pinto, C. and M. de Almeida, G. (2010) Failure Detection in a Control Final Element via Markov Model. Regional Meeting of Computational Applied Mathmatics—ERMAC, UFSJ, Ouro Branco. (in Portuguese). [4] Guimaraes Carneiro, A.L. and Porto Jr., A.C.S. (2013) Development of an Integrated Condition Monitoring and Diagnostic System for Process Control Valves Used in Nuclear Power Plant. AIDIC PHM, Milano, 872. [5] Attaway, S. (2009) MATLAB A Practical Introduction to Programming and Problem Solving. Elsevier, Amsterdam. [6] Zadeh, L.A. (1984) Coping with the Imprecision of the Real World: An Interview with Lofti Zadeh. Communications of the ACM, 27, 304-311.
http://dx.doi.org/10.1145/358027.358032 [7] Mendel, J.M. (1995) Fuzzy Logic Systems for Engineering: A Tutorial. Proceedings of the IEEE, 83, 83. [8] Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. [9] Ross, T.J. (2010) Fuzzy Logic with Engineering Applications. 3rd Edition, Wiley, Hoboken.
http://dx.doi.org/10.1002/9781119994374 [10] Mamdani, E.H. (1974) Application of Fuzzy Logic to Approximate Reasoning using Linguistic Synthesis. Department of Electrical and Electronic Engineering, Queen Mary College (University of London), London.