Abstract
With the further combination of Internet and artificial intelligence technology, remote monitoring system based on artificial intelligence and Internet technology emerges as the times require. The operation and control of modern mechanical equipment is gradually developing in the direction of automation, refinement, and integration. Therefore, to enhance the level of automation technology for remote mechanical monitoring (RMM) has been widely concerned by enterprises. The research on electronic technology of RMM has a deeper significance. RMM electronic technology is optimized on the basis of traditional mechanical monitoring, which makes it more convenient and fast, and realizes automatic monitoring fault detection at the same time. This paper mainly studies the RMM electronic technology based on KNN optimization algorithm, expounds the structure and working principle of the condition monitoring system, and analyzes the function of RTU and LabVIEW software platform. For the problem of time delay that may appear in the electronic technology of RMM, this paper proposes the time delay processing of RMM, uses buffer strategy to keep the random time delay consistent, and diagnoses the RMM system based on KNN optimization algorithm. In this paper, an intelligent fault monitoring model is established based on KNN optimization algorithm. According to the actual situation, five mechanical sample data are selected for experiments. Through the experimental training model, the mechanical pressure, oil temperature, vibration peak and engine speed of the sample machinery are observed. The experimental results show that in the fault monitoring training samples, the mechanical pressure of sample 1 is significantly abnormal compared with other samples, and the value is 0.98. The oil temperature of sample 3 is as high as 0.93, and the engine speed of sample 4 is significantly lower than other samples, which is only 0.43, sample 5 has problems in both oil temperature and engine speed. The RMM system combined with KNN optimization algorithm can observe a number of indicators in fault monitoring, and it is easy to deal with the fault phenomenon of one cause and multiple effects and one cause and multiple effects.
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Li, F., Qin, G., Zhou, W., Li, W. (2022). Remote Mechanical Monitoring Electronic Technology Based on KNN Optimization Algorithm. In: Macintyre, J., Zhao, J., Ma, X. (eds) The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIoT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 98 . Springer, Cham. https://doi.org/10.1007/978-3-030-89511-2_40
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DOI: https://doi.org/10.1007/978-3-030-89511-2_40
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