A Method for the Camera-less Remote Surveillance on Hydraulically Actuated Heavy Equipment Using IoT Environment
DOI:
https://doi.org/10.13052/ijfp1439-9776.2335Keywords:
Simulation, dynamic model, hydraulic model, heavy equipment, mobile working machines, Digital Twin, Internet of Things, IoTAbstract
The paper considers the proposed method for the camera-less remote surveillance on hydraulically actuated heavy equipment. The method uses the data about the pressure and position of hydraulic actuators as an input. These data are transmitted over the internet in the Internet of Things (IoT) environment to the IoT cloud computing platform. A simulation model consisting of hydraulic and multibody dynamics submodels composes the digital twin of the machine under surveillance. This digital twin is maintained and calculated in the cloud. It reproduces the movements of the machine and calculates the forces acting in it. Together with the GPS coordinates, these data provide the full information on the machine operation. As a result, the productivity of the machine can be estimated, the misuse can be detected, and the load history can be gathered in order to estimate the remaining life of the machine or to plan the maintenance activities. The influence of the sensor accuracy on the simulation results is evaluated. The experimental results are presented that verify the feasibility of the proposed method on the example case of hydraulic mobile crane.
Downloads
References
S. Chen, L.K. Keys, “A cost analysis model for heavy equipment,” Computers & Industrial Engineering, vol. 56(4), 2009, pp. 1276–1288.
A. Moradi Afrapoli, “A Hybrid Simulation and Optimization Approach towards Truck Dispatching Problem in Surface Mines,” PhD thesis, University of Alberta, Edmonton, Canada, 2018.
I. Lazakis, K. Dikis, A.L. Michala, G. Theotokatos, “Advanced Ship Systems Condition Monitoring for Enhanced Inspection, Maintenance and Decision Making in Ship Operations,” Transportation Research Procedia, vol. 14, 2016, pp. 1679–1688.
[4] S. Erikstad, “Merging Physics, Big Data Analytics and Simulation for the Next-Generation Digital Twins” in V. Bertram (Ed.), High-Performance Marine Vehicles, Durbanville, South Africa, 2017, pp. 141–151.
[5] W. Kritzinger, “Digital Twin in Manufacturing: : A categorical literature review and classification,” IFAC-PapersOnLine, vol. 51(11), 2018, pp. 1016–1022.
D. Zhong, H. Lv, J. Han, W. Quanrui, “A Practical Application Combining Wireless Sensor Networks and Internet of Things: Safety Management System for Tower Crane Groups,” Sensors, 14, 2014, pp. 13794–13814.
Y. Fang, “Real-time safety assistance to improve operators’ situation awareness in crane lifting operations,” PhD dissertation, Georgia Institute of Technology, 2016.
E. Sanjurjo, D. Dopico, A. Luaces, M.A. Naya, “State and force observers based on multibody models and the indirect Kalman filter,” Mechanical Systems and Signal Processing, vol. 106, pp. 210–228, 2018.
J. Kovanen, “Improving dynamic characteristics of open-loop controlled log crane,” PhD Thesis, Lappeenranta University of Technology, Lappeenranta, Finland, 2003.
J. Gottvald, “The calculation and measurement of the natural frequencies of the bucket wheel excavator SCHRS 1320/4X30,” Transport, vol. 25(3), 2010, pp. 269–277.
M. Galal Rabie, “Fluid power engineering,” New York: McGraw-Hill, 2009.
S. Andersson, A. Söderberg, and S. Björklund, “Friction models for sliding dry, boundary and mixed lubricated contacts,” Tribology int., vol. 40(4), 2007, pp. 580–587.
A. Yousefpour, C. Fung, T. Nguyen, K.P. Kadiyala, F. Jalali, A. Niakanlahiji, J. Kong, and J.P. Jue, “All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey,” Journal of Systems Architecture - Embedded Systems Design, vol. 98, 2019, pp. 289–330.
A. A. Shabana, “Computational Dynamics,” 3rd Edition, Wiley, UK, 2010.
R. Featherstone and D. Orin, “Robot Dynamics: Equations and Algorithms,” Proceedings 2000 ICRA, Millennium Conference, IEEE International Conference on Robotics and Automation, Symposia Proceedings, San Francisco, CA, USA, 2000, pp. 826–834.
J.Y.S. Luh, M.W. Walker, R.P. Paul, “On-line computational scheme for mechanical manipulators,” Trans. of the ASME J. of Dynamic Syst., Meas., and Control, vol. 102, no. 2, 1980, pp. 69–76.
R. Piche, A. Ellman, “Numerical Integration of Fluid Power Circuit Models Using Two-Stage Semi-Implicit Runge-Kutta Methods,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 208, 1994, pp. 167–175.
A. Kovartsev, “Vychislitelnaya matematika” [Computational mathematics], Ofort, Samara, Russia, 2011.
L. Luostarinen, R. Åman, and H. Handroos “Development of control interface for HIL simulation of electro-hydraulic energy converter,” Int. Rev. Modelling and Simulations (IREMOS), vol. 7(4), 2014, p. 653.
I. Malysheva, H. Handroos, V. Zhidchenko, and A. Kovartsev, “Faster than real-time simulation of a hydraulically actuated log crane,” 2018 Global Fluid Power Society PhD Symposium (GFPS).
V. Zhidchenko, I. Malysheva, H. Handroos, and A. Kovartsev, “Faster than real-time simulation of mobile crane dynamics using digital twin concept,” Journal of Physics: Conference Series, vol. 1096, 2018, p. 12071.
A. Savitzky and M. J. E. Golay, “Smoothing and Differentiation of Data by Simplified Least Squares Procedures,” Analytical Chemistry, vol. 36(8), 1964, pp. 1627–1639.