An Intelligent Autonomous System for Condition-Based Maintenance- Case Study: Control Valves
Subject Areas : Expert SystemsHamidreza Naseri 1 , Ali Shahidinejad 2 , Mostafa Ghobaei-Arani 3
1 - Qom Branch, Islamic Azad University
2 - Qom Branch, Islamic Azad University
3 - Qom Branch, Islamic Azad University
Keywords: Condition-Based Maintenance, , Neuro-fuzzy, , Autonomic computing, , control valve, ,
Abstract :
Maintenance process generally plays a vital role to achieve more benefits to the enterprises. Undoubtedly, this process has a high value-added in oil and gas industries. Process owner expectations and new technology acquisition have been changing the mindset of domain experts to the new maintenance approaches and different newer methods such as condition-based maintenance models for improving the reliability and decreasing the cost of maintenance. Because of the high dynamic behavior of the gas and the instability of the input parameters, the need to apply a model with self-healing behavior is a serious demand in the gas industry. However, to the best of our knowledge, despite its importance, there is not any comprehensive study in the literature. In this paper, we present a new neuro-fuzzy model and a self-management control loop using real world data to meet the mentioned targets for a specified control valve in a gas refinery. ANFIS model is employed for the reasoning process which has six inputs (Inlet/outlet Pressures, temperature, flow rate, controller output and valve rod displacement), and one output that is a type of failure of the control valve and the most failures are considered based on domain expert knowledge. A suitable control loop is used to unceasingly monitor, analyze, plan and finally execute the process of prediction of failures. Due to undertaken improvement, there is a considerable change in reliability and financial indices. Moreover, the proposed approach is compared with two different methods. The results show that our proposed model comprehensively improves accuracy by 24%.
[1]"ISO 14224, Petroleum, petrochemical and natural gas industries -- Collection and exchange of reliability and maintenance data for equipment." https://www.iso.org/standard/64076.html.
[2] P. Agarwal, M. Sahai, V. Mishra, M. Bag, and V. Singh, "A review of multi-criteria decision making techniques for supplier evaluation and selection," International journal of industrial engineering computations, vol. 2, no. 4, pp. 801-810, 2011.
[3] A. Garg and S. Deshmxukh, "Maintenance management: literature review and directions," Journal of quality in maintenance engineering, vol. 12, no. 3, pp. 205-238, 2006.
[4] M. Jain and K. Pathak, "Applications of artificial neural network in construction engineering and management-a review," International Journal of Engineering Technology, Management and Applied Sciences, vol. 2, no. 3, pp. 134-142, 2014.
[5] C. Friedrich, A. Lechler, and A. Verl, "Autonomous Systems for Maintenance Tasks–Requirements and Design of a Control Architecture," Procedia Technology, vol. 15, pp. 595-604, 2014.
[6] D. Goyal and B. Pabla, "Condition based maintenance of machine tools—A review," CIRP Journal of Manufacturing Science and Technology, vol. 10, pp. 24-35, 2015.
[7]A. Mérigaud and J. V. Ringwood, "Condition-based maintenance methods for marine renewable energy," Renewable and Sustainable Energy Reviews, vol. 66, pp. 53-78, 2016.
[8] M. C. O. Keizer, S. D. P. Flapper, and R. H. Teunter, "Condition-based maintenance policies for systems with multiple dependent components: A review," European Journal of Operational Research, vol. 261, no. 2, pp. 405-420, 2017.
[9] R. Ahmad and S. Kamaruddin, "An overview of time-based and condition-based maintenance in industrial application," Computers & Industrial Engineering, vol. 63, no. 1, pp. 135-149, 2012.
[10] M. C. Carnero, "Condition Based Maintenance in small industries," IFAC Proceedings Volumes, vol. 45, no. 31, pp. 199-204, 2012.
[11] Q. Zhu, H. Peng, B. Timmermans, and G.-J. van Houtum, "A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs," International Journal of Production Economics, vol. 193, pp. 365-380, 2017.
[12] M. C. O. Keizer, R. H. Teunter, J. Veldman, and M. Z. Babai, "Condition-based maintenance for systems with economic dependence and load sharing," International Journal of Production Economics, vol. 195, pp. 319-327, 2018.
[13] P. Do, A. Voisin, E. Levrat, and B. Iung, "A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions," Reliability Engineering & System Safety, vol. 133, pp. 22-32, 2015.
[14] B. Liu, S. Wu, M. Xie, and W. Kuo, "A condition-based maintenance policy for degrading systems with age-and state-dependent operating cost," European Journal of Operational Research, vol. 263, no. 3, pp. 879-887, 2017.
[15] J. Poppe, R. N. Boute, and M. R. Lambrecht, "A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds," European Journal of Operational Research, vol. 268, no. 2, pp. 515-532, 2018.
[16] P. Guariente, I. Antoniolli, L. P. Ferreira, T. Pereira, and F. Silva, "Implementing autonomous maintenance in an automotive components manufacturer," Procedia Manufacturing, vol. 13, pp. 1128-1134, 2017.
[17] M. Lewandowski and S. Oelker, "Towards autonomous control in maintenance and spare part logistics–challenges and opportunities for preacting maintenance concepts," Procedia Technology, vol. 15, pp. 333-340, 2014.
[18] G. Walter and S. D. Flapper, "Condition-based maintenance for complex systems based on current component status and Bayesian updating of component reliability," Reliability Engineering & System Safety, vol. 168, pp. 227-239, 2017.
[19] R. Kothamasu and S. H. Huang, "Adaptive Mamdani fuzzy model for condition-based maintenance," Fuzzy Sets and Systems, vol. 158, no. 24, pp. 2715-2733, 2007.
[20] M. Engeler, D. Treyer, D. Zogg, K. Wegener, and A. Kunz, "Condition-based Maintenance: Model vs. Statistics a Performance Comparison," Procedia CIRP, vol. 57, pp. 253-258, 2016.
[21] M. Ghobaei-Arani, R. Khorsand and M. Ramezanpour, "An autonomous resource provisioning framework for massively multiplayer online games in cloud environment", Journal of Network and Computer Applications, Volume 142, pp. 76-97, 2019.
[22] M. Aslanpour, S. Dashti, M. Ghobaei-Arani and A. Rahmanian, "Resource provisioning for cloud applications: a 3-D, provident and flexible approach", The Journal of Supercomputing, vol. 74, no. 12, pp. 6470-6501, 2018.
[23] M. Ghobaei-Arani, A. Rahmanian, M. Shamsi and A. Rasouli-Kenari, "A learning-based approach for virtual machine placement in cloud data centers", International Journal of Communication Systems, vol. 31, no. 8, p. e3537, 2018.
[24] A. Sharifi, A. Vosolipour, M. A. Sh, and M. Teshnehlab, "Hierarchical Takagi-Sugeno type fuzzy system for diabetes mellitus forecasting," in Machine Learning and Cybernetics, 2008 International Conference on, 2008, vol. 3: IEEE, pp. 1265-1270.
[25] N. Walia, H. Singh, and A. Sharma, "ANFIS: Adaptive neuro-fuzzy inference system-a survey," International Journal of Computer Applications, vol. 123, no. 13, 2015.
