Abstract
Real-time monitoring of machines is vital for enhanced performance and safety in industries. Gears are common components that interconnect mechanical parts that allow each part in a mechanical system to be engaged. They are mainly used to transmit kinetic energy and transform rotational speed. Due to the importance of gears, the degradation or failure of its performance affects the function of the machine resulting in the unplanned breakdown of equipment, This inevitably leads to economic losses and personnel safety issues. Therefore, it is of great significance to recognize industrial safety with respect to equipment management. In this paper, we presented a distributed architecture for monitoring the gears and reporting its faults. The monitoring of gears and gearboxes can alleviate safety issues and improve maintenance plans.
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References
Jin, C. (1999). Vibration monitoring and fault diagnosis of mechanical equipment. Shanghai: Shanghai Jiaotong University Press.
Pan, W., Yuan, Y., Sandberg, H., Gonçalves, J., & Stan, G.-B. (2015). Online fault diagnosis for nonlinear power systems. Automatica, 55, 27–36. https://doi.org/10.1016/j.automatica.2015.02.032
Zhigao, L., Xueqing, Q., & Haiquan, T. (2006). Domestic status and development direction of gear fault diagnosis. Mining Machinery, 34(1).
Li, W., Zhu, Z., Jiang, F., Zhou, G., & Chen, G. (2015). Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method. Mechanical Systems and Signal Processing, 50–51, 414–426. https://doi.org/10.1016/j.ymssp.2014.05.034
Wenyi, L. (2000). Research on vibration monitoring and fault diagnosis of wind turbine. Chongqing: Chongqing University.
Chen, Y., Lee, G., Shu, L., & Crespi, N. (2016). Industrial internet of things-based collaborative sensing intelligence: Framework and research challenges. Sensors, 16(2), 215.
Jan, M. A., Khan, F., Alam, M., & Usman, M. (2017). A payload-based mutual authentication scheme for Internet of Things. Future Generation Computer Systems.
Guoan, Y. (2016). Practical technology of fault diagnosis of mechanical equipment. Beijing: Petrochemical Press.
Qingfeng, W., Jianfeng, Y., & Wenbin, L. (2010). Development and application of intelligent decision system for press industrial equipment maintenance. Chinese Journal of Mechanical Engineering, 24(46), 168–177.
Weiming, L., Yugang, C., & Guangpei, C. (2016). Sensor-based gear vibration monitoring and reliability detection method. Mechanical and Electrical Engineering Technology, (5), 43–46.
Shufen, F., & Wenyuan, L. (2001). Research on intelligent decision support system for equipment maintenance management. Systems Engineering-Theory and Practice(12), 53–59.
Acknowledgements
The paper is supported by the Science and Technology Project of Maoming City (No. 2017316, No. 2017318).
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Li, W., Chen, Y., Alam, M. (2019). Distributed Monitoring Architecture for Industrial Safety Based on Gear Fault Diagnosis. In: Jan, M., Khan, F., Alam, M. (eds) Recent Trends and Advances in Wireless and IoT-enabled Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-99966-1_22
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DOI: https://doi.org/10.1007/978-3-319-99966-1_22
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