Manufacturing Technology 2023, 23(4):426-435 | DOI: 10.21062/mft.2023.059

Exploration of Key Technologies of Intelligent Inspection Robots in the Application of Automatic Energy Meter Verification Line

Xurong Jin ORCID...1, Xinrui Zhang ORCID...1, Zhiqiang Cheng ORCID...1, Yunpeng Li ORCID...1, Bo Fan ORCID...1, Xu Chen ORCID...1, Xiangwei Wang ORCID...2, Mingqiang Wang ORCID...2
1 State Grid Ningxia Marketing Service Center (State Grid Ningxia Metrology Center), China
2 Comarvel Intelligent Technology Company Limited, China

There are many drawbacks and inconveniences in the application of human power in the energy meter calibration line. In order to achieve a standardized level of operation and to improve the efficiency and quality of the automated meter testing line, this paper applies the intelligent inspection robot to the automated meter testing line and discusses the key technologies involved. Based on the texture characteristics of the screws on the energy meter cover, a screw coordinate positioning method based on the texture center of gravity method is designed as a machine vision technique for intelligent inspection robots. Based on the feedforward controller transfer function and feedback system open-loop transfer function, combined with PI controller, a feedforward-feedback composite servo position control strategy is designed to complete the release action of the robot end controller. Pressure sensor on the robot end claw controller, integrated servo drive with current sensor. The Kalman filter method of static estimation is used to fuse and process multi-source data information to realize the grasping action of the robot end controller. The test results of the key technical performance and economic and time benefits of the robot show that the recognition success rate and grasping success rate of the robot for energy meters are as high as 100%, and it takes a total of 54s to complete each grasping and releasing action. The maximum error in each direction is 4.9mm, 5.2mm and 5.1mm respectively, and the maximum error in angle is only 1.25 degrees. The working manpower is reduced by as much as 93.16%, the average expenditure of inspection cost is only 1.24 yuan, and the floor space is reduced to 700 square meters. In summary, the study can ensure a high level of consistency in the quality of energy meters, improve the efficiency of calibration and production, and create greater economic benefits while providing a solid technical guarantee for the large-scale construction and stable and reliable operation of the power grid.

Keywords: Intelligent inspection robot, Automatic calibration line for energy meters, Machine vision technology, Servo control technology, Torque control technology

Received: May 13, 2023; Revised: July 13, 2023; Accepted: August 9, 2023; Prepublished online: August 9, 2023; Published: September 5, 2023  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Jin X, Zhang X, Cheng Z, Li Y, Fan B, Chen X, et al.. Exploration of Key Technologies of Intelligent Inspection Robots in the Application of Automatic Energy Meter Verification Line. Manufacturing Technology. 2023;23(4):426-435. doi: 10.21062/mft.2023.059.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. PENG, L., LI, J., ZHAO, J., et al. (2022). Automatic Verification Flow Shop Scheduling of Electric Energy Meters Based on an Improved Q-Learning Algorithm, Energies, Vol. 15. No. 5, pp. 1-11. Go to original source...
    2. SEFERI, Y., BLAIR, S.M., MESTER, C., et al. (2020). Power Quality Measurement and Active Harmonic Power in 25 kV 50 Hz AC Railway Systems, Energies, Vol. 13, No. 21, pp. 1-17. Go to original source...
    3. JG, A., DV, B. (2020). Verification of the technical equipment degradation method using a hybrid reinforcement learning tree-artificial neural network system, Tribology International, Vol. 153, pp. 106618. Go to original source...
    4. TORRES, E.S., CELEITA, D., SRIRAMULA, S., et al. (2020). Safety Integrity Level Verification Model for IED Protection Schemes, IEEE Transactions on Industry Applications, Vol. 56, No. 4, pp. 4329-4336. Go to original source...
    5. SOUREN, T., ROSE, E., GROEPENHOFF, H. (2021). Comparison of Two Metabolic Simulators Used for Gas Exchange Verification in Cardiopulmonary Exercise Test Carts, Frontiers in physiology, Vol. 12, pp. 667386. Go to original source...
    6. PERNILLA, L., SOFIA, J., PETRUS, H., et al. (2022). Nasal Lavage Fluid as a Biomedical Sample for Verification of Chlorine Exposure, Journal of Analytical Toxicology, Vol. 46, No. 5, pp. 559-566. Go to original source...
    7. ZHAO, Y., DOU, Z., YU, Z., et al. (2022). Study on the Optimal Dispatching Strategy of a Combined Cooling, Heating and Electric Power System Based on Demand Response, Energies, Vol. 15, No. 10, pp. 1-18. Go to original source...
    8. BABONNEAU, F., BARRERA, J., TOLEDO, J. (2021). Decarbonizing the Chilean Electric Power System: A Prospective Analysis of Alternative Carbon Emissions Policies, Energies, Vol. 14, No. 16, pp. 1-16. Go to original source...
    9. WU, S., DONG, Z. (2022). An Auxiliary Decision-Making System for Electric Power Intelligent Customer Service Based on Hadoop, Scientific Programming, Vol. 2022, pp. 1-11. Go to original source...
    10. DÍAZ, S. (2022). Can Shared Control Improve Overtaking Performance? Combining Human and Automation Strengths for a Safer Maneuver, Sensors, Vol. 22, No. 23, pp. 9093. Go to original source...
    11. DING, S.Y., LIU, J.L., YUE, M.H. (2021). The Use of ZigBee Wireless Communication Technology in Industrial Automation Control, Wireless Communications and Mobile Computing, Vol. 2021, No. 6, 1-11. Go to original source...
    12. MOCANU, D.A., BĂDESCU, V. BUCUR, C., et al. (2020). PLC Automation and Control Strategy in a Stir-ling Solar Power System, Energies, Vol. 13, No. 8, pp. 1-19. Go to original source...
    13. GARANGER, K., KHAMVILAI, T., FERON, E. (2020). Validating Feedback Control to Meet Stiffness Requirements in Additive Manufacturing, IEEE Transactions on Control Systems Technology, Vol. 99, pp. 1-8. Go to original source...
    14. NDONDA, G.K., SADRE, R. (2020). Network trace generation for flow-based IDS evaluation in control and automation systems, International Journal of Critical Infrastructure Protection, Vol. 31, No. 3, pp. 100385. Go to original source...
    15. SINGH, S., FOZDAR, M., MALIK, H., et al. (2020). Impacts of Renewable Sources of Energy on Bid Modeling Strategy in an Emerging Electricity Market Using Oppositional Gravitational Search Algorithm, Energies, Vol. 14, No. 18, pp. 1-20. Go to original source...
    16. SOLIBAKKE, P.B. (2022). Projecting and Forecasting the Latent Volatility for the Nasdaq OMX Nordic/Baltic Financial Electricity Market Applying Stochastic Volatility Market Characteristics, Energies, Vol. 15, No. 10, pp. 1-20. Go to original source...
    17. MIHET-POPA, L., SAPONARA, S. (2021). Power Converters, Electric Drives and Energy Storage Systems for Electrified Transportation and Smart Grid, Energies, Vol. 14, No. 14, pp. 1-5. Go to original source...
    18. CARBONE, G. (2022). Analysis of the Leap Motion Controller's Performance in Measuring Wrist Rehabilitation Tasks Using an Industrial Robot Arm Reference, Sensors, Vol. 22, No. 13, pp. 4880. Go to original source...
    19. MEIDUTE-KAVALIAUSKIENE, I., YLDZ, B., IDEM, E., et al. (2021). The Effect of COVID-19 on Air-line Transportation Services: A Study on Service Robot Usage Intention, Sustainability, Vol. 13, No. 22, pp. 1-19. Go to original source...
    20. ALIAJ, K., FEENEY, G.M., SUNDARALINGAM, B., et al. (2020). Replicating dynamic humerus motion using an industrial robot, PLos One, Vol. 15, No. 11, pp. e0242005. Go to original source...
    21. RASHID, T., ALI, A., CHU, Y.M. (2021). Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection, PLoS One, Vol. 16, No. 2, pp. e0246738. Go to original source...
    22. KACZMAREK, W., LOTYS, B., BORYS, S., et al. (2021). Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode, Sensors, Vol. 21, No. 7, pp. 1-20. Go to original source...
    23. SKOCZEŃ, M., OCHMAN, M., SPYRA, K., et al. (2021). Obstacle Detection System for Agricultural Mo-bile Robot Application Using RGB-D Cameras. Sensors, Vol. 21, No. 16, pp. 5292. Go to original source...
    24. WU, C.H., LU, C.Y., ZHAN, J.W., et al. (2020). Using Long Short-Term Memory for Building Outdoor Agricultural Machinery, Frontiers in Neurorobotics, Vol. 14, pp. 27. Go to original source...
    25. ATEFI, A., GE, Y., PITLA, S., et al. (2021). Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives, Frontiers in Plant Science, Vol. 12, pp. 1082-1082. Go to original source...
    26. MOSHIRI, M., LOALDI, D., ZANINI, F., et al. (2021). Analysis of an as-built metal additively manufactured tool cavity insert performance and advantages for plastic injection moulding, Journal of Manufacturing Processes, Vol. 61, No. 5-8, pp. 369-382. Go to original source...
    27. SEPTIARINI, A., SUNYOTO, A., HAMDANI, H., et al. (2021). Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features, Scientia Horticulturae, Vol. 286, pp. 110245. Go to original source...
    28. KHATUN, F., AIZU, Y., NISHIDATE, I. (2021). In Vivo Transcutaneous Monitoring of Hemoglobin Derivatives Using a Red-Green-Blue Camera-Based Spectral Imaging Technique, International Journal of Molecular Sciences, Vol. 22, No. 4, pp. 1528. Go to original source...
    29. TABASSUM, M.F., AKGÜL, A. AKRAM, S., et al. (2022). Optimal solution of engineering design problems through differential gradient evolution plus algorithm: a hybrid approach, Physica Scripta, Vol. 97, No. 1, pp. 014002-014002. Go to original source...
    30. WANG, T., THOMASSON, J.A., YANG, C., et al. (2020). Automatic Classification of Cotton Root Rot Disease Based on UAV Remote Sensing, Remote Sensing, Vol. 12, No. 8, pp. 1310. Go to original source...
    31. WANG, Y., YAO, D., HE, J., et al. (2020). A Stabilization Method Based on an Adaptive Feedforward Con-troller for the Underactuated Bipedal Walking with Variable Step-Length on Compliant Discontinuous Ground, Complexity, Vol. 2020, No. 2, pp. 1-16. Go to original source...
    32. WAN, N., LI, D., HOVAKIMYAN, N. (2020). A Simplified Approach to Analyze Complementary Sensitivity Tradeoffs in Continuous-Time and Discrete-Time Systems, IEEE Transactions on Automatic Control, Vol. 65, No. 4, pp. 1697-1703. Go to original source...
    33. MATU©, M., BECHNÝ, V., JOCH, R., DRBÚL, M., HOLUBJÁK, J., CZÁN, A., NOVÁK, M. & ©AJGALÍK, M. 2023. Geometric Accuracy of Components Manufactured by SLS Technology Regarding the Orientation of the Model during 3D Printing. Manufacturing Technology, 23, 233-40. Go to original source...
    34. NAGAMINE, G., JEONG, B.G., FERREIRA, T., et al. (2020). Efficient Optical Gain in Spherical Quan-tum Wells Enabled by Engineering Bi-exciton Interactions, ACS Photonics, Vol. 7, No. 8, pp. 2252-2264. Go to original source...
    35. YANG, Y., DONG, Y. (2022). Dynamics of matter-wave solitons in three-component Bose-Einstein condensates with time-modulated interactions and gain or loss effect, Physica Scripta, Vol. 97, No. 2, pp. 025201-025201. Go to original source...
    36. CEDZO, M., JOCH, R., TIMKO, P., HOLUBJÁK, J., CZÁNOVÁ, T. & ©AJGALÍK, M. 2023. Topology Optimization of Gripping Jaws of Industrial Robot. Manufacturing Technology, 23, 25-31. Go to original source...
    37. MOSTAFA, S., ZEKRY, A., YOUSSEF, A., et al. (2022). Raspberry Pi Design and Hardware Implementation of Fuzzy-PI Controller for Three-Phase Grid-Connected Inverter Energies, Vol. 15, No. 3, pp. 1-22. Go to original source...
    38. OU, C., LAM, S.K., SUN, D., et al. (2021). SNR-Centric Power Trace Extractors for Side-Channel Attacks, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 40, No. 4, pp. 620-632. Go to original source...
    39. SONG, L., SUN, H., XU, K., SHI, X. & ZHOU, Y. 2022. Path Planning under The Hull Bottom of Painting Robot Based on Heuristic Multi-robot Cooperation in Ship Manufacturing. Manufacturing Technology, 22, 218-30. Go to original source...
    40. CSERVENÁK, Á. 2021. Path and Trajectory Planning for an Automated Carrier Vehicle Equipped with two Conveyor Belts used in Manufacturing Supply. Manufacturing Technology, 21, 164-83. Go to original source...
    41. ZHANG, Y., LI, T., LI, B., et al. (2020). Fault Detection Method of AC Charging Pile in Coastal Cities Based on Kalman Filtering Algorithm, Journal of Coastal Research, Vol. 104, No. sp1. Go to original source...
    42. KHEBLI, A., AGUIB, S., NOUREDDINE, C., LALLIA, K. & MOUNIR, M. 2021. Mathematical modeling and numerical simulation of the buckling stability behavior of hybrid beam. Manufacturing Technology, 21, 793-804. Go to original source...
    43. KANG, S.Y., KIM, T.H., CHUNG, W.Z. (2020). Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks, Sensors (Basel, Switzerland), Vol. 20, No. 4, pp. 1159. Go to original source...
    44. WERNIK, C. (2020). Extended Multi WLS Method for Lossless Image Coding, Entropy, Vol. 22, No. 9, pp. 919. Go to original source...

    This is an open access article distributed under the terms of the Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content