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Real-time online drilling vibration analysis using data mining

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationProceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019
Number of pages6
ISBN (Electronic)9781450371414
Publication statusPublished - 19 Jul 2019
Publication typeA4 Article in a conference publication
EventInternational Conference on Data Science and Information Technology - Seoul, Korea, Republic of
Duration: 19 Jul 201921 Jul 2019


ConferenceInternational Conference on Data Science and Information Technology
CountryKorea, Republic of


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.


  • Data mining, Drilling stages, Real-time, Statistical analysis

Publication forum classification

Field of science, Statistics Finland