Signal drift modeling and analysis of an intelligent instrument using a multivariate adaptive regression splines technique

References

• Khanafer, M.; Shirmohammadi, S. Applied AI in Instrumentation and Measurement: The Deep Learning Revolution. IEEE Instrum. Meas. Mag. 2020, 23, 10–17. DOI: 10.1109/MIM.2020.9200875.
   Web of Science ®Google Scholar
• Xiao, Y.; Shao, H.; Feng, M.; Han, T.; Wan, J.; Liu, B. Towards Trustworthy Rotating Machinery Fault Diagnosis via Attention Uncertainty in Transformer. J. Manuf. Syst. 2023, 70, 186–201. DOI: 10.1016/j.jmsy.2023.07.012.
   Web of Science ®Google Scholar
• Barbariol, T.; Feltresi, E.; Susto, G. A. Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection. Energies 2020, 13, 3136. DOI: 10.3390/en13123136.
   Web of Science ®Google Scholar
• Chauhan, Y.; Shrestha, N. B.; Santhosh, T. V.; Shrivastava, V.; Ramteke, P. K.; Vinod, G.; Chattopadhyay, J. Accelerated Life Testing and Performance Evaluation of Smart Pressure Transmitters for Use in Nuclear Power Plants. Presented at the. 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, Aug 29–31, 2018. DOI: 10.1109/ICRITO.2018.8748604.
   Google Scholar
• Zhao, S.; Guo, C.; Ke, C.; Zhou, Y.; Shu, X. Temperature Drift Compensation of Fiber Strapdown Inertial Navigation System Based on GSA-SVR. Measurement 2022, 195, 111117. DOI: 10.1016/j.measurement.2022.111117.
   Web of Science ®Google Scholar
• Yan, S.; Shao, H.; Min, Z.; Peng, J.; Cai, B.; Liu, B. FGDAE: A New Machinery Anomaly Detection Method towards Complex Operating Conditions. Reliab. Eng. Syst. Saf. 2023, 236, 109319. DOI: 10.1016/j.ress.2023.109319.
   Web of Science ®Google Scholar
• Chaudhuri, T.; Wu, M.; Zhang, Y.; Liu, P.; Li, X. An Attention-Based Deep Sequential GRU Model for Sensor Drift Compensation. IEEE Sensors J. 2021, 21, 7908–7917. DOI: 10.1109/JSEN.2020.3044388.
   Web of Science ®Google Scholar
• Prakash, A.; Sharma, N.; Sharma, S. Novel Force Myography Sensor to Measure Muscle Contractions for Controlling Hand Prostheses. Instrum. Sci. Technol. 2020, 48, 43–62. DOI: 10.1080/10739149.2019.1655441.
   Web of Science ®Google Scholar
• Wang, P.; Li, G.; Gao, Y. A Compensation Method for Gyroscope Random Drift Based on Unscented Kalman Filter and Support Vector Regression Optimized by Adaptive Beetle Antennae Search Algorithm. Appl. Intell. 2023, 53, 4350–4365. DOI: 10.1007/s10489-022-03734-7.
   Web of Science ®Google Scholar
• Yu, W.; Tu, W.; Kim, I. Y.; Mechefske, C. A Nonlinear-Drift-Driven Wiener Process Model for Remaining Useful Life Estimation considering 3 Sources of Variability. Reliab. Eng. Syst. Saf. 2021, 212, 107631. DOI: 10.1016/j.ress.2021.107631.
   Web of Science ®Google Scholar
• Chen, X.; Shao, H.; Xiao, Y.; Yan, S.; Cai, B.; Liu, B. Collaborative Fault Diagnosis of Rotating Machinery via Dual Adversarial Guided Unsupervised Multi-Domain Adaptation Network. Mech. Syst. Signal Proc. 2023, 198, 110427. DOI: 10.1016/j.ymssp.2023.110427.
   Web of Science ®Google Scholar
• Zhang, Y.; Piao, L.; Wang, Y. Compensation of Temperature Drift of Micro Gyroscope by Polynomial Fitting Algorithm. Presented at the 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Xi’an, China, Oct 15–17. 2021.
   Google Scholar
• Chaabene, W. B.; Flah, M.; Nehdi, M. L. Machine Learning Prediction of Mechanical Properties of Concrete: Critical Review. Constr. Build. Mater. 2020, 260, 119889. DOI: 10.1016/j.conbuildmat.2020.119889.
   Web of Science ®Google Scholar
• Cho, W. J.; Kim, H. J.; Jung, D. H.; Han, H. J.; Cho, Y. Y. Hybrid Signal-Processing Method Based on Neural Network for Prediction of NO3, K, Ca, and Mg Ions in Hydroponic Solutions Using an Array of Ion-Selective Electrodes. Sensors 2019, 19, 5508. DOI: 10.3390/s19245508.
   PubMed Web of Science ®Google Scholar
• Han, Z.; Hong, L.; Meng, J.; Li, Y.; Gao, Q. Temperature Drift Modeling and Compensation of Capacitive Accelerometer Based on AGA-BP Neural Network. Measurement 2020, 164, 108019. DOI: 10.1016/j.measurement.2020.108019.
   Web of Science ®Google Scholar
• Karimipour, A.; Bagherzadeh, S. A.; Taghipour, A.; Abdollahi, A.; Safaei, M. R. A Novel Nonlinear Regression Model of SVR as a Substitute for ANN to Predict Conductivity of MWCNT-CuO/Water Hybrid Nanofluid Based on Empirical Data. Physica A 2019, 521, 89–97. DOI: 10.1016/j.physa.2019.01.055.
   Web of Science ®Google Scholar
• Novi, T.; Capitani, R.; Annicchiarico, C. An Integrated Artificial Neural Network–Unscented Kalman Filter Vehicle Sideslip Angle Estimation Based on Inertial Measurement Unit Measurements. Proc. Inst. Mech. Eng., Part D J. Automob. Eng. 2019, 233, 1864–1878. DOI: 10.1177/0954407018790646.
   Web of Science ®Google Scholar
• Li, L. N.; Liu, X. F.; Yang, F.; Xu, W. M.; Wang, J. Y.; Shu, R. A Review of Artificial Neural Network Based Chemometrics Applied in Laser-Induced Breakdown Spectroscopy Analysis. Spectrochim. Acta Part B 2021, 180, 106183. DOI: 10.1016/j.sab.2021.106183.
   Web of Science ®Google Scholar
• Friedman, J. H. Multivariate Adaptive Regression Splines. Ann. Stat. 1991, 19, 1–67.
   Web of Science ®Google Scholar
• Chen, W. H.; Lo, H. J.; Aniza, R.; Lin, B. J.; Park, Y. K.; Kwon, E. E.; Sheen, H. K.; Grafilo, L. A. D. R. Forecast of Glucose Production from Biomass Wet Torrefaction Using Statistical Approach along with Multivariate Adaptive Regression Splines, Neural Network and Decision Tree. Appl. Energy 2022, 324, 119775. DOI: 10.1016/j.apenergy.2022.119775.
   Web of Science ®Google Scholar
• Bui, D. T.; Hoang, N. D.; Pham, T. D.; Ngo, P. T. T.; Hoa, P. V.; Minh, N. Q.; Tran, X. T.; Samui, P. A New Intelligence Approach Based on GIS-Based Multivariate Adaptive Regression Splines and Metaheuristic Optimization for Predicting Flash Flood Susceptible Areas at High-Frequency Tropical Typhoon Area. J. Hydrol. 2019, 575, 314–326. DOI: 10.1016/j.jhydrol.2019.05.046.
   Web of Science ®Google Scholar
• Ju, X.; Rosenberger, J. M.; Chen, V. C.; Liu, F. Global Optimization on Non-Convex Two-Way Interaction Truncated Linear Multivariate Adaptive Regression Splines Using Mixed Integer Quadratic Programming. Inf. Sci. 2022, 597, 38–52. DOI: 10.1016/j.ins.2022.03.041.
   Web of Science ®Google Scholar
• Dey, P.; Das, A. K. Application of Multivariate Adaptive Regression Spline-Assisted Objective Function on Optimization of Heat Transfer Rate around a Cylinder. Nucl. Eng. Technol. 2016, 48, 1315–1320. DOI: 10.1016/j.net.2016.06.011.
   Web of Science ®Google Scholar
• Sirimontree, S.; Jearsiripongkul, T.; Lai, V. Q.; Eskandarinejad, A.; Lawongkerd, J.; Seehavong, S.; Thongchom, C.; Nuaklong, P.; Keawsawasvong, S. Prediction of Penetration Resistance of a Spherical Penetrometer in Clay Using Multivariate Adaptive Regression Splines Model. Sustainability 2022, 14, 3222. DOI: 10.3390/su14063222.
   Web of Science ®Google Scholar
• Naser, A. H.; Badr, A. H.; Henedy, S. N.; Ostrowski, K. A.; Imran, H. Application of Multivariate Adaptive Regression Splines (MARS) Approach in Prediction of Compressive Strength of Eco-Friendly Concrete. Case Stud. Constr. Mater. 2022, 17, e01262. DOI: 10.1016/j.cscm.2022.e01262.
   Google Scholar
• Tiryaki, S.; Tan, H.; Bardak, S.; Kankal, M.; Nacar, S.; Peker, H. Performance Evaluation of Multiple Adaptive Regression Splines, Teaching–Learning Based Optimization and Conventional Regression Techniques in Predicting Mechanical Properties of Impregnated Wood. Eur. J. Wood Prod. 2019, 77, 645–659. DOI: 10.1007/s00107-019-01416-9.
   Web of Science ®Google Scholar
• Huang, K.; Zhang, X.; Karimi, N. Real-Time Prediction for IC Aging Based on Machine Learning. IEEE Trans. Instrum. Meas. 2019, 68, 4756–4764. DOI: 10.1109/TIM.2019.2899477.
   Web of Science ®Google Scholar
• Weber, G. W.; Batmaz, I.; Köksal, G.; Taylan, P.; Yerlikaya-Özkurt, F. CMARS: A New Contribution to Nonparametric Regression with Multivariate Adaptive Regression Splines Supported by Continuous Optimization. Inverse Probl. Sci. Eng. 2012, 20, 371–400. DOI: 10.1080/17415977.2011.624770.
   Web of Science ®Google Scholar
• Kuter, S.; Weber, G. W.; Akyürek, Z.; Özmen, A. Inversion of Top of Atmospheric Reflectance Values by Conic Multivariate Adaptive Regression Splines. Inverse Probl. Sci. Eng. 2015, 23, 651–669. DOI: 10.1080/17415977.2014.933828.
   Web of Science ®Google Scholar
• Onak, Ö. N.; Dogrusoz, Y. S.; Weber, G. W. Evaluation of Multivariate Adaptive Non-Parametric Reduced-Order Model for Solving the Inverse Electrocardiography Problem: A Simulation Study. Med. Biol. Eng. Comput. 2019, 57, 967–993. DOI: 10.1007/s11517-018-1934-9.
   PubMed Web of Science ®Google Scholar
• Çevik, A.; Weber, G. W.; Eyüboğlu, B. M.; Oğuz, K. K, Alzheimer’s Disease Neuroimaging Initiative. Voxel-MARS: A Method for Early Detection of Alzheimer’s Disease by Classification of Structural Brain MRI. Ann. Oper. Res. 2017, 258, 31–57. DOI: 10.1007/s10479-017-2405-7.
   Web of Science ®Google Scholar
• Kalaycı, B.; Özmen, A.; Weber, G. W. Mutual Relevance of Investor Sentiment and Finance by Modeling Coupled Stochastic Systems with MARS. Ann. Oper. Res. 2020, 295, 183–206. DOI: 10.1007/s10479-020-03757-8.
   Web of Science ®Google Scholar
• Özmen, A.; Kropat, E.; Weber, G. W. Robust Optimization in Spline Regression Models for Multi-Model Regulatory Networks under Polyhedral Uncertainty. Optimization 2017, 66, 2135–2155. DOI: 10.1080/02331934.2016.1209672.
   Web of Science ®Google Scholar
• Adnan, R. M.; Liang, Z.; Heddam, S.; Zounemat-Kermani, M.; Kisi, O.; Li, B. Least Square Support Vector Machine and Multivariate Adaptive Regression Splines for Streamflow Prediction in Mountainous Basin Using Hydro-Meteorological Data as Inputs. J. Hydrol. 2020, 586, 124371. DOI: 10.1016/j.jhydrol.2019.124371.
   Web of Science ®Google Scholar
• Zhang, W.; Goh, A. T. C. Multivariate Adaptive Regression Splines and Neural Network Models for Prediction of Pile Drivability. Geosci. Front. 2016, 7, 45–52. DOI: 10.1016/j.gsf.2014.10.003.
   Web of Science ®Google Scholar
• Shiau, J.; Lai, V. Q.; Keawsawasvong, S. Multivariate Adaptive Regression Splines Analysis for 3D Slope Stability in Anisotropic and Heterogenous Clay. J. Rock Mech. Geotech. Eng. 2023, 15, 1052–1064. DOI: 10.1016/j.jrmge.2022.05.016.
   Web of Science ®Google Scholar
• Bui, D. T.; Hoang, N. D.; Samui, P. Spatial Pattern Analysis and Prediction of Forest Fire Using New Machine Learning Approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination Optimization: A Case Study at Lao Cai Province (Viet Nam). J. Environ. Manage 2019, 237, 476–487. DOI: 10.1016/j.jenvman.2019.01.108.
   PubMed Web of Science ®Google Scholar
• Kao, L. J.; Chiu, C. C. Application of Integrated Recurrent Neural Network with Multivariate Adaptive Regression Splines on SPC-EPC Process. J. Manuf. Syst. 2020, 57, 109–118. DOI: 10.1016/j.jmsy.2020.07.020.
   Web of Science ®Google Scholar
• De Andres, J.; Lorca, P.; de Cos Juez, F. J.; Sánchez-Lasheras, F. Bankruptcy Forecasting: A Hybrid Approach Using Fuzzy c-Means Clustering and Multivariate Adaptive Regression Splines (MARS). Exp. Syst. Appl. 2011, 38, 1866–1875. DOI: 10.1016/j.eswa.2010.07.117.
   Web of Science ®Google Scholar
• Cao, M. T.; Nguyen, N. M.; Wang,.; W.; C. Using an Evolutionary Heterogeneous Ensemble of Artificial Neural Network and Multivariate Adaptive Regression Splines to Predict Bearing Capacity in Axial Piles. Eng. Struct. 2022, 268, 114769. DOI: 10.1016/j.engstruct.2022.114769.
   Web of Science ®Google Scholar
• Zhou, M.; Pan, Z.; Liu, Y.; Zhang, Q.; Cai, Y.; Pan, H. Leak Detection and Location Based on ISLMD and CNN in a Pipeline. IEEE Access 2019, 7, 30457–30464. DOI: 10.1109/ACCESS.2019.2902711.
   Web of Science ®Google Scholar
• Wang, Z.; Qian, Z.; Lu, J.; Wu, P. Effects of Flow Rate and Rotational Speed on Pressure Fluctuations in a Double-Suction Centrifugal Pump. Energy 2019, 170, 212–227. DOI: 10.1016/j.energy.2018.12.112.
   Web of Science ®Google Scholar