Prediction of shallow bit position based on vibration signal monitoring of bit broken rock
The speed of drilling has a great relationship with the rock breaking efficiency of the bit. Based on the above background, the purpose of this article is to predict the position of shallow bit based on the vibration signal monitoring of bit broken rock. In this article, first, the mechanical resear...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2021-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147721991708 |
| _version_ | 1797763890040274944 |
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| author | Jinping Yu Deyong Zou |
| author_facet | Jinping Yu Deyong Zou |
| author_sort | Jinping Yu |
| collection | DOAJ |
| description | The speed of drilling has a great relationship with the rock breaking efficiency of the bit. Based on the above background, the purpose of this article is to predict the position of shallow bit based on the vibration signal monitoring of bit broken rock. In this article, first, the mechanical research of drill string is carried out; the basic changes of the main mechanical parameters such as the axial force, torque, and bending moment of drill string are clarified; and the dynamic equilibrium equation theory of drill string system is analyzed. According to the similarity criterion, the corresponding relationship between drilling process parameters and laboratory test conditions is determined. Then, the position monitoring test system of the vibration bit is established. The acoustic emission signal and the drilling force signal of the different positions of the bit in the process of vibration rock breaking are collected synchronously by the acoustic emission sensor and the piezoelectric force sensor. Then, the denoised acoustic emission signal and drilling force signal are analyzed and processed. The mean value, variance, and mean square value of the signal are calculated in the time domain. The power spectrum of the signal is analyzed in the frequency domain. The signal is decomposed by wavelet in the time and frequency domains, and the wavelet energy coefficients of each frequency band are extracted. Through the wavelet energy coefficient calculated by the model, combined with the mean, variance, and mean square error of time-domain signal, the position of shallow buried bit can be analyzed and predicted. Finally, by fitting the results of indoor experiment and simulation experiment, it can be seen that the stress–strain curve of rock failure is basically the same, and the error is about 3.5%, which verifies the accuracy of the model. |
| first_indexed | 2024-03-12T19:47:50Z |
| format | Article |
| id | doaj.art-13035bab092c4815963659359b4b7b89 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T19:47:50Z |
| publishDate | 2021-01-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-13035bab092c4815963659359b4b7b892023-08-02T03:23:15ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772021-01-011710.1177/1550147721991708Prediction of shallow bit position based on vibration signal monitoring of bit broken rockJinping YuDeyong ZouThe speed of drilling has a great relationship with the rock breaking efficiency of the bit. Based on the above background, the purpose of this article is to predict the position of shallow bit based on the vibration signal monitoring of bit broken rock. In this article, first, the mechanical research of drill string is carried out; the basic changes of the main mechanical parameters such as the axial force, torque, and bending moment of drill string are clarified; and the dynamic equilibrium equation theory of drill string system is analyzed. According to the similarity criterion, the corresponding relationship between drilling process parameters and laboratory test conditions is determined. Then, the position monitoring test system of the vibration bit is established. The acoustic emission signal and the drilling force signal of the different positions of the bit in the process of vibration rock breaking are collected synchronously by the acoustic emission sensor and the piezoelectric force sensor. Then, the denoised acoustic emission signal and drilling force signal are analyzed and processed. The mean value, variance, and mean square value of the signal are calculated in the time domain. The power spectrum of the signal is analyzed in the frequency domain. The signal is decomposed by wavelet in the time and frequency domains, and the wavelet energy coefficients of each frequency band are extracted. Through the wavelet energy coefficient calculated by the model, combined with the mean, variance, and mean square error of time-domain signal, the position of shallow buried bit can be analyzed and predicted. Finally, by fitting the results of indoor experiment and simulation experiment, it can be seen that the stress–strain curve of rock failure is basically the same, and the error is about 3.5%, which verifies the accuracy of the model.https://doi.org/10.1177/1550147721991708 |
| spellingShingle | Jinping Yu Deyong Zou Prediction of shallow bit position based on vibration signal monitoring of bit broken rock International Journal of Distributed Sensor Networks |
| title | Prediction of shallow bit position based on vibration signal monitoring of bit broken rock |
| title_full | Prediction of shallow bit position based on vibration signal monitoring of bit broken rock |
| title_fullStr | Prediction of shallow bit position based on vibration signal monitoring of bit broken rock |
| title_full_unstemmed | Prediction of shallow bit position based on vibration signal monitoring of bit broken rock |
| title_short | Prediction of shallow bit position based on vibration signal monitoring of bit broken rock |
| title_sort | prediction of shallow bit position based on vibration signal monitoring of bit broken rock |
| url | https://doi.org/10.1177/1550147721991708 |
| work_keys_str_mv | AT jinpingyu predictionofshallowbitpositionbasedonvibrationsignalmonitoringofbitbrokenrock AT deyongzou predictionofshallowbitpositionbasedonvibrationsignalmonitoringofbitbrokenrock |