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Real-time online drilling vibration analysis using data mining

Tutkimustuotosvertaisarvioitu

Standard

Real-time online drilling vibration analysis using data mining. / Zare, Marzieh; Huova, Mikko; Visa, Ari; Launis, Sirpa.

Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019. ACM, 2019. s. 175-180.

Tutkimustuotosvertaisarvioitu

Harvard

Zare, M, Huova, M, Visa, A & Launis, S 2019, Real-time online drilling vibration analysis using data mining. julkaisussa Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019. ACM, Sivut 175-180, Seoul, Etelä-Korea, 19/07/19. https://doi.org/10.1145/3352411.3352439

APA

Zare, M., Huova, M., Visa, A., & Launis, S. (2019). Real-time online drilling vibration analysis using data mining. teoksessa Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019 (Sivut 175-180). ACM. https://doi.org/10.1145/3352411.3352439

Vancouver

Zare M, Huova M, Visa A, Launis S. Real-time online drilling vibration analysis using data mining. julkaisussa Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019. ACM. 2019. s. 175-180 https://doi.org/10.1145/3352411.3352439

Author

Zare, Marzieh ; Huova, Mikko ; Visa, Ari ; Launis, Sirpa. / Real-time online drilling vibration analysis using data mining. Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019. ACM, 2019. Sivut 175-180

Bibtex - Lataa

@inproceedings{3e143e8b41814e838bf2492371bfde91,
title = "Real-time online drilling vibration analysis using data mining",
abstract = "While the data mining intermediaries play a critical role in the rock drilling industry, they also tend to provide an optimized real-time model for the drilling systems. In addition, proper online tool condition monitoring (OTOM) methods can improve the drilling performance by accessing real-time data. Hence, OTOM methods assist depreciating error and detect unspecified faults at early stages. In this study, we proposed appropriate OTOM algorithms to develop and enhance the quality of real-time systems and provide a solution to detect and categorize various stages of drilling operation with the aid of vibration signals (especially in terms of acceleration or velocity). In particular, the proposed methods in this article perform based on statistical approaches. Therefore, in order to recognize the drilling stages, we measured the Root Mean Square (RMS) values corresponding to the acceleration signals. In the meantime, we also succeeded to distinguish the drilling stages by employing estimated power spectral density (PSD) in the frequency domain. The acquired results in this publication confirm the real-time prediction and classification potential of the proposed methods for the different drilling stages and especially for the rock drilling engineering.",
keywords = "Data mining, Drilling stages, Real-time, Statistical analysis",
author = "Marzieh Zare and Mikko Huova and Ari Visa and Sirpa Launis",
year = "2019",
month = "7",
day = "19",
doi = "10.1145/3352411.3352439",
language = "English",
pages = "175--180",
booktitle = "Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019",
publisher = "ACM",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Real-time online drilling vibration analysis using data mining

AU - Zare, Marzieh

AU - Huova, Mikko

AU - Visa, Ari

AU - Launis, Sirpa

PY - 2019/7/19

Y1 - 2019/7/19

N2 - While the data mining intermediaries play a critical role in the rock drilling industry, they also tend to provide an optimized real-time model for the drilling systems. In addition, proper online tool condition monitoring (OTOM) methods can improve the drilling performance by accessing real-time data. Hence, OTOM methods assist depreciating error and detect unspecified faults at early stages. In this study, we proposed appropriate OTOM algorithms to develop and enhance the quality of real-time systems and provide a solution to detect and categorize various stages of drilling operation with the aid of vibration signals (especially in terms of acceleration or velocity). In particular, the proposed methods in this article perform based on statistical approaches. Therefore, in order to recognize the drilling stages, we measured the Root Mean Square (RMS) values corresponding to the acceleration signals. In the meantime, we also succeeded to distinguish the drilling stages by employing estimated power spectral density (PSD) in the frequency domain. The acquired results in this publication confirm the real-time prediction and classification potential of the proposed methods for the different drilling stages and especially for the rock drilling engineering.

AB - While the data mining intermediaries play a critical role in the rock drilling industry, they also tend to provide an optimized real-time model for the drilling systems. In addition, proper online tool condition monitoring (OTOM) methods can improve the drilling performance by accessing real-time data. Hence, OTOM methods assist depreciating error and detect unspecified faults at early stages. In this study, we proposed appropriate OTOM algorithms to develop and enhance the quality of real-time systems and provide a solution to detect and categorize various stages of drilling operation with the aid of vibration signals (especially in terms of acceleration or velocity). In particular, the proposed methods in this article perform based on statistical approaches. Therefore, in order to recognize the drilling stages, we measured the Root Mean Square (RMS) values corresponding to the acceleration signals. In the meantime, we also succeeded to distinguish the drilling stages by employing estimated power spectral density (PSD) in the frequency domain. The acquired results in this publication confirm the real-time prediction and classification potential of the proposed methods for the different drilling stages and especially for the rock drilling engineering.

KW - Data mining

KW - Drilling stages

KW - Real-time

KW - Statistical analysis

U2 - 10.1145/3352411.3352439

DO - 10.1145/3352411.3352439

M3 - Conference contribution

SP - 175

EP - 180

BT - Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019

PB - ACM

ER -