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Academic Journal of Engineering and Technology Science, 2022, 5(3); doi: 10.25236/AJETS.2022.050309.

The Application of Data Mining Technology in the Predictive Maintenance for Oil and Gas Equipment

Author(s)

Fei Zhu

Corresponding Author:
Fei Zhu
Affiliation(s)

Honeywell China Limited, Shang Hai, China

Abstract

Data mining is a new research topic in intelligent predictive maintenance by searching, counting, and processing a large amount of data to obtain valuable information. With the rapid development of the petroleum industry in recent years, automation and intelligent oil and gas equipment have been constantly updated and applied with a significant role in the daily production scheduling process control in the oil and gas fields. In this context, this work first discussed the basic concept of data mining technology and predictive maintenance, then presented the implementation route of data mining technology in predictive maintenance, and finally proposed the predictive maintenance scheme of deep hole drilling tools based on the two-mode algorithm. This study mainly discussed the basic problems and key technologies of data mining technology in the predictive maintenance of oil and gas equipment, aiming to provide new solutions to the equipment fault prediction.

Keywords

Data mining; Oil and gas equipment; Predictive maintenance; Bimodal algorithm

Cite This Paper

Fei Zhu. The Application of Data Mining Technology in the Predictive Maintenance for Oil and Gas Equipment. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 3: 45-48. https://doi.org/10.25236/AJETS.2022.050309.

References

[1] Abbasi, T., Lim, K. H., & San Yam, K. (2019, April). Predictive maintenance of oil and gas equipment using recurrent neural network. In Iop conference series: Materials science and engineering (Vol. 495, No. 1, p. 012067). IOP Publishing.

[2] Cline, B., Niculescu, R. S., Huffman, D., & Deckel, B. (2017, January). Predictive maintenance applications for machine learning. In 2017 annual reliability and maintainability symposium (RAMS) (pp. 1-7). IEEE.

[3] Abbasi, T., Lim, K. H., Rosli, N. S., Ismail, I., & Ibrahim, R. (2018, August). Development of predictive maintenance interface using multiple linear regression. In 2018 International Conference on Intelligent and Advanced System (ICIAS) (pp. 1-5). IEEE.

[4] Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.

[5] Shanbhag, V. V., Meyer, T. J., Caspers, L. W., & Schlanbusch, R. (2021). Failure Monitoring and Predictive Maintenance of Hydraulic Cylinder—State-of-the-Art Review. IEEE/ASME Transactions on Mechatronics, 26(6), 3087-3103.