An Intelligent System for Vibrodiagnostics of Oil and Gas Equipment
Source abstract
Abstract
An intelligent system for the diagnostics and monitoring of oil and gas equipment with a computerized device for identification measurements of vibration signals is described. The system operates based on the expert knowledgebases of the FRASH- and S-methods and the databases of tabulated quantitative and qualitative properties of objects. It has been shown that the temporal properties of the condition of objects and their autocorrelation functions are clearly described by identification parameters. The example of applying the intelligent technology of identification measurements to the vibration-based diagnostics of an HM 12500-210 no. 12 pump unit has shown that such diagnostics and monitoring is more efficient as it recognizes not only states that include groups of flaws with similar characteristics but also the flaws themselves. An instance of the intelligent computer-aided system for the diagnostics and monitoring of oil and gas equipment using modern information–communication technologies is presented.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Engineering Diagnostics and Reliability · Oil and Gas Production Techniques · Industrial Engineering and Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (9)
Show source-provided references
1. Okhtilev, MYu., Sokolov, MYu., and Yusupov, RM., Intellektual’nye tekhnologii monitoringa i upravleniya strukturnoi dinamikoi slozhnykh tekhnicheskikh ob’ektov (Intelligent Technologies for Monitoring and Control of Dynamics of Complex Technical Objects), Moscow: Nauka, 2006.
2. www.ros-diagnostics.ru
3. Akhtar Hussain, Seung-Jae Lee, Myeon-Song Choi, and Fouad Brikci, An expert system for acoustic diagnosis of power circuit breakers and on-load tap changers, Expert Syst. Appl., 2015, vol. 42, no. 24, p. 9426–9433.10.1016/j.eswa.2015.07.079 ↗
4. Futao Zhu, Zhongmin Deng, and Junfeng Zhang, An integrated approach for structural damage identification using wavelet neuro-fuzzy model, Expert Syst. Appl., 2013, vol. 40, no. 18, pp. 7415–7427.10.1016/j.eswa.2013.06.078 ↗
5. Satish Mohanty, Karunesh Kumar, Gupta Kota., and Solomon Raju, Multi-channel vibro-acoustic fault analysis of ball bearing using wavelet based multi-scale principal component analysis, in IEEE Twenty First Natl. Conf. Commun. (NCC), Mumbai, 2015, p. 37–40.
6. Subhransu, Sekhar Dash, M Arun Bhaskar, Bijaya Ketan Panigrahi, and Swagatam Das, Artificial intelligence and evolutionary computations in engineering systems, in Proc. ICAIECES, Springer, 2015.
7. Klikushin, Yu.N. and Koshekov, KT., Metody i sredstva identifikatsionnykh izmerenii signalov (Methods and Means of Identification Signal Measurements), Petropavlovsk: Kozybaev North Kaz. State Univ., 2007.
8. Koshekov, KT., Klikushin, Yu.N., Kobenko, VYu., Evdokimov, Yu.K., and Demyanenko, AV., Fuel cell diagnostics using identification measurement theory, J Fuel Cell Sci. Technol., 2014, 11(5) 051003, paper no. FC-14-1027.10.1115/1.4027395 ↗
9. Klikushin, Yu.N., Koshekov, KT., Kobenko, VYu., and Trunin, ES., An algorithm for evaluating the state of a generating unit based on the identification measurements of vibrosignals, Russ. J. Nondestr. Test., 2014, vol. 50, No. 7, pp. 413–418.10.1134/S1061830914070055 ↗