Machine Diagnosis based Multi-Agent Technology by Autonomous Sensor with Energy Harvesting

Munawir Munawir, Devi Rimadhani Agustini, Rahmawati Rahmawati, Abdullah Muadz Nadzir Anzhar

Abstract


A machine diagnosis system based on multi-agent technology is proposed. We mainly focus on developing a multi-agent system for rotating machinery fault diagnosis by vibration sensor with energy harvesting. To estimate the inner rotation of machines in plant frequency analysis is frequently used. Our approach for diagnosis is agent-based, where vibration data is analyzed by a set of software agents coming from distributed servers to the user side. Another feature of this study is the development of autonomous vibration sensors. It earns electric power from vibration so that we are free from battery maintenance, and continuous online monitoring is enabled. Based on the implementation results of the existing multi-agent system design prototype, the harvesting sensor working process can produce total energy of 205µW with a working cycle of about 6.5 minutes, the energy harvester works, and the power accumulates continuously.


Keywords


Energy harvesting; Machine diagnosis; Multi-agent; Rotating machinery; Sensor

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References


Lu, S., Zhou, P., Wang, X., Liu, Y., Liu, F., and Zhao, J. (2018). Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network. Journal of Sound and Vibration, 414(1), 81-96.

Shao, S., McAleer, S., Yan, R., and Baldi, P. (2018). Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 15(4), 2446-2455.

Wu, C., Jiang, P., Ding, C., Feng, F., and Chen, T. (2019). Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Computers in Industry, 108(1), 53-61.

Ke, L., Yiya, L., Jingjing, W., Lei, S., and Peng, C. (2017). Fault detection and diagnosis of rotating machinery using modified particle filter, Journal of Vibroengineering, 19(5), 3395-3412.

Mellit, A., Tina, G. M., and Kalogirou, S. A. (2018). Fault detection and diagnosis methods for photovoltaic systems: a review. Renewable and Sustainable Energy Reviews, 91(1), 1-17.

Zhang, Z., Mehmood, A., Shu, L., Huo, Z., Zhang, Y., and Mukherjee, M. (2018). A survey on fault diagnosis in wireless sensor networks. IEEE Access, 6(1), 11349-11364.

Dorri, A., Kanhere, S. S., and Jurdak, R. (2018). Multi-agent systems: a survey. IEEE Access, 6(1), 28573-28593.

Rizk, Y., Awad, M., and Tunstel, E. W. (2019). Cooperative heterogeneous multi-robot systems: a survey. ACM Computing Surveys (CSUR), 52(2), 1-31.

Hussain, N., Wang, H. H., Buckingham, C. D., and Zhang, X. (2020). Software agent-centric semantic social network for cyber-physical interaction and collaboration. International Journal of Software Engineering and Knowledge Engineering, 30(06), 859-893.

Baldoni, M., Bergenti, F., Seghrouchni, A. E. F., and Winikoff, M. (2021). Special issue on current trends in research on software agents and agent-based software systems. Autonomous Agents and Multi-Agent Systems, 35(29), 1-4.

Ge, X., and Han, Q. L. (2017). Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism. IEEE Transactions on Industrial Electronics, 64(10), 8118-8127.

Wang, Z., and Hong, T. (2020). Reinforcement learning for building controls: the opportunities and challenges. Applied Energy, 269(1), 1-29.

Liu, R., Yang, B., Zio, E., and Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: a review. Mechanical Systems and Signal Processing, 108(1), 33-47.

Abar, S., Theodoropoulos, G. K., Lemarinier, P., and O’Hare, G. M. (2017). Agent based modelling and simulation tools: a review of the state-of-art software. Computer Science Review, 24(1), 13-33.

Xie, J., and Liu, C. C. (2017). Multi-agent systems and their applications. Journal of International Council on Electrical Engineering, 7(1), 188-197.

Yang, F., Du, L., Chen, W., Li, J., Wang, Y., and Wang, D. (2017). Hybrid energy harvesting for condition monitoring sensors in power grids. Energy, 118(1), 435-445.

Bhowmick, D., Manna, M., and Chowdhury, S. K. (2018). Estimation of equivalent circuit parameters of transformer and induction motor from load data. IEEE Transactions on Industry Applications, 54(3), 2784-2791.

Sun, H., Zhang, Y., Zhang, J., Sun, X., and Peng, H. (2017). Energy harvesting and storage in 1D devices. Nature Reviews Materials, 2(6), 1-12.

Wei, C., and Jing, X. (2017). A comprehensive review on vibration energy harvesting: modelling and realization. Renewable and Sustainable Energy Reviews, 74(1), 1-18.

Abdelkareem, M. A., Xu, L., Ali, M. K. A., Elagouz, A., Mi, J., Guo, S., Liu, Y., and Zuo, L. (2018). Vibration energy harvesting in automotive suspension system: A detailed review. Applied Energy, 229(1), 672-699.




DOI: https://doi.org/10.17509/coelite.v1i1.43818

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