Application of electromagnetic interference-BP technology in mechanical noise control of small DC electrodes
1
Department of Basic Teaching, Henan Polytechnic Institute, Nanyang 473000, China
2
Xinjiang Production and Construction Corps 13th Division Senior Vocational High School, Hami City 439000, China
Submission date: 2024-07-10
Final revision date: 2025-07-18
Acceptance date: 2025-07-24
Online publication date: 2025-08-01
Publication date: 2025-08-01
KEYWORDS
noise controlelectromagnetic interferencebackpropagation neural networkmechanical noisesmall DC electrode
TOPICS
ABSTRACT
To effectively reduce the causes of mechanical noise generated by small DC electrodes during operation and the electromagnetic interference effects on surrounding equipment, a mechanical noise control method that combines piezoelectric impedance technology and back-propagation neural networks is proposed. In the process, small DC motor electrodes were used as the research object, and the sources of mechanical noise generated by the motor were analyzed. At the same time, different analysis software was used to simulate and model the stator and rotor of the motor. The results show that when different algorithms are run on the training set and test set, when the amount of data increases to 560 and 1120 respectively, the method constructed in the experiment has the maximum fitness value, with values as high as 98.98% and 97.86%. When the training set is run, when the running time increases to 0.894s, the accuracy of the method constructed in the experiment to control mechanical noise reaches 91.68%. The application effect shows that when the material of the stator shell is steel, the occurrence of the maximum natural frequency of the stator is affected by the elastic modulus, which is far greater than the influence of the material density.
FUNDING
This research received no external funding.
REFERENCES (29)
1.
Ranjbar E, Yaghubi M, Abolfazl Suratgar A. Robust adaptive sliding mode control of a MEMS tunable capacitor based on dead-zone method. Automatika: Časopis Za Automatiku, Mjerenje, Elektroniku, Računarstvo I Komunikacije, 2020;61(4):587-601. https://doi.org/10.1080/000511....
2.
Simon K, Vicent M, Addah K, Bamutura D, Atwiine B, Nanjebe D, Mukama AO. Comparison of deep learning techniques in detection of sickle cell disease. Artificial Intelligence and Applications. 2023;1(4): 252-259. https://doi.org/10.47852/bonvi....
3.
Zhai L, Yang S, Hu G, Lv M. Optimal design method of high voltage dc power supply EMI filter considering source impedance of motor controller for electric vehicle. IEEE Transactions on Vehicular Technology. 2022;72(1):367-381. https://doi.org/10.1109/TVT.20....
4.
Preethi P, Mamatha HR. Region-Based convolutional neural network for segmenting text in Epigraphical images. Artificial Intelligence and Applications. 2023;1(2):119-127. https://doi.org/10.47852/bonvi....
5.
Nelis JLD, Bose U, Broadbent JA, Hughes J, Sikes A, Anderson A, Colgrave ML. Biomarkers and biosensors for the diagnosis of noncompliant pH, dark cutting beef predisposition, and welfare in cattle. Comprehensive Reviews in Food Science and Food Safety. 2022;21(3):2391-2432. https://doi.org/10.1111/1541-4....
6.
Xu C, Wang J, Chen D, Chen J, Liu B, Qi W, Zheng X. The electrochemical seismometer based on fine-tune sensing electrodes for undersea exploration. IEEE Sensors Journal. 2020;20(15):8194-8202. https://doi.org/10.1109/JSEN.2....
7.
Abd Aziz MA, Saidon MS, Romli MIF, Othman SM, Mustafa WA, Manan MR, Aihsan MZA. Review on BLDC motor application in electric vehicle (EV) using battery, supercapacitor and hybrid energy storage system: efficiency and future prospects. Journal of Advanced Research in Applied Sciences and Engineering Technology. 2023;30(2):41-59. https://doi.org/10.37934/arase....
8.
Abdullah Y, Shaffer J, Hu B, Hall B, & Arfaei B. Hurst-exponent-based detection of high-impedance DC arc events for 48-V systems in vehicles. IEEE Transactions on Power Electronics. 2020;36(4): 3803-3813. https://doi.org/10.1109/TPEL.2....
9.
Jiang Y, Liu L, Chen L, Zhang Y, He Z, Zhang W, Qin Y. Flexible and stretchable dry active electrodes with PDMS and silver flakes for bio-potentials sensing systems. IEEE Sensors Journal. 2021;21(10):12255-12268. https://doi.org/10.1109/JSEN.2....
10.
Krasecki VK, Sharma A, Cavell AC, Forman C, Guo SY, Jensen ET, Goldsmith RH. The role of experimental noise in a hybrid classical-molecular computer to solve combinatorial optimization problems. ACS Central Science. 2023;9(7):1453-1465. ttps://doi.org/10.1021/acscentsci.3c00515.
11.
Xu H, Zhang W, Deng J, Rabault J. Active flow control with rotating cylinders by an artificial neural network trained by deep reinforcement learning. Journal of Hydrodynamics. 2020;32(2): 254-258. https://doi.org/10.1007/s42241....
12.
Cao J, Wei X, Su Y, Shi H, Zhou D. Experimental analysis of electromagnetic pulse effects on engine fuel electronic control system. International Journal of Applied Electromagnetics and Mechanics. 2020; 65(6):1-13. https://doi.org/10.3233/JAE-19....
13.
Ge MY, Wang GW, Jia Y. Influence of the Gaussian colored noise and electromagnetic radiation on the propagation of subthreshold signals in feedforward neural networks. Science China Technological Sciences, 2021, 64(4): 847-857. https://doi.org/10.1007/s11431....
14.
Wang J, Liu Y, Jin Y, Zhang Y. Control of hydraulic power system by mixed neural network PID in unmanned walking platform. Journal of Beijing Institute of Technology. 2020;29(3):273-282.
15.
Wang Q, Wang X. Parameters optimization of the heating furnace control systems based on BP neural network improved by genetic algorithm. IEEE Internet of Things Journal. 2020;2(2):75-80. https://doi.org/10.32604/jiot.....
16.
Xue B, Li Y, Cheng Z, Yang S, Xie L, Qin S. Directional electromagnetic interference shielding based on step-wise asymmetric conductive networks. Nano-Micro Letters. 2022;14(1):262-277. https://doi.org/10.1007/s40820....
17.
Charles D. The Lead-Lag Relationship between international food prices, freight rates, and Trinidad and Tobago’s food inflation: a support vector regression analysis. Green and Low-Carbon Economy. 2023;1(2):94-103. https://doi.org/10.47852/BONVI....
18.
Vafamand N, Arefi MM. Robust neural network‐based backstepping landing control of quadrotor on moving platform with stochastic noise. International Journal of Robust and Nonlinear Control. 2022;32(4): 2007-2026. https://doi.org/10.1002/rnc.59....
19.
Kanwisher N, Khosla M, Dobs K. Using artificial neural networks to ask ‘why’questions of minds and brains. Trends in Neurosciences. 2023;46(3):240-254. https://doi.org/10.1016/j.tins....
20.
Goel A, Goel AK, Kumar A. The role of artificial neural network and machine learning in utilizing spatial information. Spatial Information Research. 2023;31(3):275-285. https://doi.org/10.1007/s41324....
21.
Kurani A, Doshi P, Vakharia A, Shah M. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science. 2023; 10(1):183-208. https://doi.org/10.1007/s40745....
22.
Khan J, Lee E, Kim K. A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network. CAAI Transactions on Intelligence Technology. 2023;8(4): 1124-1139. https://doi.org/10.1049/cit2.1....
23.
Shen H. Assessment of financial risk pre-alarm mechanism based on financial ecosystem using BPNN and genetic algorithm. Soft Computing. 2023;27(24): 19265-19279. https://doi.org/10.1007/s00500....
24.
Su K, Zhang J, Zhang J, Yan T, Mei G. Optimisation of current collection quality of high-speed pantograph-catenary system using the combination of artificial neural network and genetic algorithm. Vehicle System Dynamics. 2023;61(1):260-285. https://doi.org/10.1080/004231....
25.
Tanhaeean M, Ghaderi S F, Sheikhalishahi M. Optimization of backpropagation neural network models for reliability forecasting using the boxing match algorithm: electro-mechanical case. Journal of Computational Design and Engineering. 2023;10(2): 918-933. https://doi.org/10.1093/jcde/q....
26.
Shen LB, Tian LP. A static load position identification method for optical fiber-composite structures based on particle swarm optimization-back Propagation neural network algorithm. Measurement and Control. 2023;56(3-4):820-831. https://doi.org/10.1177/002029....
27.
Chi X, Quan S, Chen J, Wang YX, He H. Proton exchange membrane fuel cell-powered bidirectional DC motor control based on adaptive sliding-mode technique with neural network estimation. International Journal of Hydrogen Energy. 2020;45(39):20282-20292. https://doi.org/10.1016/j.ijhy....
28.
Wei M, Chen K, Li S, Cao J, Ali A. An intelligent method based on time–frequency analysis and deep learning semantic segmentation for investigating the electromagnetic pulse features of engine digital controllers. IEEE Transactions on Electromagnetic Compatibility. 2022;65(1):257-270. https://doi.org/10.1109/TEMC.2....
29.
Zhao D, Shen K, Chen L, Wang Z, Liu W, Yang T, Wheeler P. Improved active damping stabilization of DAB converter interfaced aircraft DC microgrids using neural network-based model predictive control. IEEE Transactions on Transportation Electrification. 2021;8(2):1541-1552. https://doi.org/10.1109/TTE.20....
Share
RELATED ARTICLE
| eISSN: | 2449-5220 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
