Abstract
The current elevator status monitoring systems basically realize the monitoring of elevator status by collecting signals from the main board of the elevator. However it is costly and lacks universality, which also requires invasive installation. To address the above problems, a non-invasive intelligent monitoring method is proposed in this paper for elevator operation status. The method decomposes the acceleration signal into vertical and horizontal components, estimates the dynamics of the elevator using Kalman filter, performs vibration analysis on the horizontal components, establishes a baseline for normal operation, and automatically calibrates the sensors by combining the operating characteristics of the elevator. The traceless Kalman filter based on fused SLAM was performed to couple the sensor information and track the real-time position of the elevator. The effectiveness and robustness of the method are verified in actual operation, and the problem of elevator position error accumulation is basically solved without installing fiducials. The designed non-intrusive elevator status intelligent monitoring method is low-cost and universal, which is of practical significance for promoting on-demand elevator maintenance.
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Acknowledgments
This work was supported by the Next Generation Internet Technology Innovation Project Of Celtic Network (No. NGII20181206) , the National Natural Science Foundation of China (No.61976150), the Key R & D Projects of Shanxi Province (No. 201803D31038).
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Ma, Y., Yang, Y., Wang, Q., Li, X., Xu, Z., Li, H. (2021). Research on Non Intrusive Intelligent Monitoring System for Elevator State. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1452. Springer, Singapore. https://doi.org/10.1007/978-981-16-5943-0_25
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DOI: https://doi.org/10.1007/978-981-16-5943-0_25
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