Accurate prediction of the drilling rate of penetration (ROP) prediction is an enormously important step to optimize the controllable parameters of anti-impact drilling robot (AIDR), and single-step forecasting cannot adjust parameters in advance. The feeding speed and rotational speed are crucial parameters that reflect the degree of obstruction presented by the geological environment to the AIDR. Furthermore, the vibration data can provide insight into the instantaneous impact of vibration on the drilling process. Therefore, accurate multi-step prediction for ROP is crucial for ensuring the efficient and safe propulsion of AIDR. This paper proposes a multi-step prediction strategy combining drilling multi-sensor signals and a bidirectional long-short-term memory combined with a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) for AIDR drilling parameters. Initially, the vibration, feeding speed, and rotation speed signals are denoised using an improved variable mode decomposition (IVMD) and wavelet threshold approach. Subsequently, the feeding speed, rotational speed and vibration signals are weighted and normalized by self-adaptation. Given the basis, the CNN-BiLSTM neural network is utilized to make a multi-step prediction, ultimately realizing the multi-step feeding speed and rotation speed prediction. Compared with the recent approach, the result shows that the proposed strategy has superior prediction accuracy.
QiaoW.Analysis and measurement of multifactor risk in underground coal mine accidents based on coupling theory. Reliab Eng Syst Saf2021; 208: 107433.
2.
DaiJGongFHuangD, et al. Quantitative evaluation method of rockburst prevention effect for anchoring rock masses around deep-buried tunnels. Tunnelling Undergr Space Technol2025; 156: 106268.
3.
WangGFPangYHRenHW.Intelligent coal mining pattern and technological path. J Min Strat Control Eng2020; 2(1): 013501.
4.
ChenYFLiXJWuHB, et al. Control status and development trend of rockburst mechanism and prevention in China. Coal Sci Technol Mag2021; 42(5): 70–75.
5.
FanHLaiXDuS, et al. Distributed monitoring with integrated probability PCA and mRMR for drilling processes. IEEE Trans Instrum Meas2022; 71: 1–13.
6.
KhoshoueiMBagherpourR.Measurement, prediction, and modeling of the drilling specific energy by soft rock properties during the drilling operation. Measurement2023; 222: 113679.
7.
RenJJiangJZhouC, et al. Research on adaptive feature optimization and drilling rate prediction based on real-time data. Geoenergy Sci Eng2024; 242: 213247.
8.
ZhouYLuCZhangM, et al. A novel rate of penetration model based on support vector regression and modified bat algorithm. IEEE Trans Ind Inform2023; 19(5): 6659–6668.
9.
GanCCaoW-HLiuK-Z, et al. Dynamic optimization-based intelligent control system for drilling rate of penetration (ROP): Simulation and industrial application. IEEE Trans Ind Inform2024; 20(3): 3695–3702.
10.
MehradMBajolvandMRamezanzadehA, et al. Developing a new rigorous drilling rate prediction model using a machine learning technique. J Pet Sci Eng2020; 192: 107338.
11.
WuXLaiXHuJ, et al. Improvement of rate of penetration in drilling process based on TCN-vibration recognition. IEEE Trans Instrum Meas2024; 73: 1–12.
12.
KoohsariAKalatehjariRMoosazadehS, et al. Measurement and enhancing prediction of EPBM torque using actual machine data. Measurement2023; 223: 113780.
13.
ZhouYChenXZhaoH, et al. A novel rate of penetration prediction model with identified condition for the complex geological drilling process. J Process Control2021; 100: 30–40.
14.
ZhaoYNoorbakhshAKoopialipoorM, et al. A new methodology for optimization and prediction of rate of penetration during drilling operations. Eng Comput2020; 36: 587–595.
15.
Al-AbdulJabbarAMahmoudAAElkatatnyS, et al. Artificial neural networks-based correlation for evaluating the rate of penetration in a vertical carbonate formation for an entire oil field. J Pet Sci Eng2022; 208: 109693.
16.
ChenJCaiMShenQ, et al. A predictive model based on singular spectrum analysis and ARIMA model with adaptive orders for shield tunneling machine cutterhead torque. IEEE Sens J2023; 23(22): 27645–27657.
17.
BizhaniMKuruE.Towards drilling rate of penetration prediction: Bayesian neural networks for uncertainty quantification. J Pet Sci Eng2022; 219: 111068.
18.
NajjarpourMJalalifarHNorouzi-ApourvariS.Half a century experience in rate of penetration management: application of machine learning methods and optimization algorithms - a review. J Pet Sci Eng2022; 208: 109575.
19.
OssaiCIDuruUI.Applications and theoretical perspectives of artificial intelligence in the rate of penetration. Petroleum2022; 8(2): 237–251.
20.
RenCHuangWGaoD.Predicting rate of penetration of horizontal drilling by combining physical model with machine learning method in the China Jimusar Oil Field. SPE J2023; 28(06): 2713–2736.
21.
ShayganKJamshidiS.Prediction of rate of penetration in directional drilling using data mining techniques. Geoenergy Sci Eng2023; 221: 111293.
22.
AkdulumAKayirY.Prediction of thrust force in indexable drilling of aluminum alloys with machine learning algorithms. Measurement2023; 222: 113655.
23.
YangXWuMLuC, et al. Prediction of rate of penetration based on drilling conditions identification for drilling process. Neurocomputing2024; 579: 127439.
24.
ChenXWengCDuX, et al. Prediction of the rate of penetration in offshore large-scale cluster extended reach wells drilling based on machine learning and big-data techniques. Ocean Eng2023; 285: 115404.
25.
QinYZhouJXiaoD, et al. High-precision cutterhead torque prediction for tunnel boring machines using an attention-based embedded LSTM neural network. Measurement2024; 224: 113888.
26.
LawalAIKwonSOnifadeM.Prediction of rock penetration rate using a novel antlion optimized ANN and statistical modelling. J Afr Earth Sci2021; 182: 104287.
27.
SharifinasabMHEmami NiriMMasroorM.Developing GAN-boosted artificial neural networks to model the rate of drilling bit penetration. Appl Soft Comput2023; 136: 110067.
28.
KaoIFZhouYChangLC, et al. Exploring a long short-term memory based encoder-decoder framework for multi-step-ahead flood forecasting. J Hydrol2020; 583: 124631.
29.
Rodrigues MorenoSGomes da SilvaRCocco MarianiV, et al. Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network. Energy Convers Manag2020; 213: 112869.
30.
ZamaniAAEtedaliS.Seismic response prediction of open- and closed-loop structural control systems using a multi-state-dependent parameter estimation approach. Int J Comput Methods2022; 19(05): 2250006.
31.
WangTLeungHZhaoJ, et al. Multiseries featural LSTM for partial periodic time-series prediction: a case study for steel industry. IEEE Trans Instrum Meas2020; 69: 5994–6003.
32.
ShiGQinCTaoJ, et al. A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque. Knowl Syst2021; 228: 107213.
33.
KumarAKovacevicAStosicN.Lifecycle cost analysis and performance evaluation of multi-stage screw compressors. Proc Inst Mech Eng Part C: J Mech Eng Sci2025; 239(9): 3144–3154.
34.
ViehwegJWorthmannKMäderP.Parameterizing echo state networks for multi-step time series prediction. Neurocomputing2023; 522: 214–228.
35.
MirjaliliS.The ant lion optimizer. Adv Eng Softw2015; 83: 80–98.
36.
ZhongHLiuJWangL, et al. Bearing fault diagnosis based on kernel independent component analysis and antlion optimization. Trans Inst Meas Contr2021; 43(16): 3573–3587.
37.
ZamaniAAEtedaliS.New formulas for optimal design of TMD using genetic programming method and their application to control of seismic-excited structures. J Struct Constr Eng2023; 10(4): 67–86.
38.
ZhangXWangFZhouY, et al. The optimized local sparse parallel multi-channel deep convolutional neural network-LSTM and the application in bearing fault diagnosis under noise and variable working condition. Proc Inst Mech Eng Part C: J Mech Eng Sci2025; 239(1): 205–218.
39.
XiYZhangHLiB.Wear particles image enhancement using long short-term memory 3D recurrent reconstruction neural network (LSTM 3D-R2N2). Proc Inst Mech Eng Part C: J Mech Eng Sci2024; 238(22): 10864–10872.
