Real-Time Optimization and Fault Diagnosis Algorithms for State Event Analysis in Elevator Group Control Systems
Keywords:
Fault Diagnosis, Optimization, Control System, Event Analysis, Trickle Timer, ElevatorAbstract
Real-time optimization and fault diagnosis algorithms are essential components of elevator group control systems, ensuring efficient operation and timely detection of malfunctions. These algorithms continuously analyze the state events within elevator systems, such as passenger demand, elevator positions, and system performance metrics, to optimize elevator dispatching and minimize passenger waiting times. Moreover, they employ fault diagnosis techniques to detect anomalies or failures in elevator components, such as motors, sensors, or control systems, enabling prompt maintenance interventions and ensuring system reliability. By combining real-time optimization with fault diagnosis, elevator group control systems can enhance operational efficiency, passenger safety, and overall system performance, contributing to a seamless and reliable vertical transportation experience. This paper presented an approach to real-time optimization and fault diagnosis in elevator group control systems, integrating Trickle Transition State Event Analysis (TTSEA) techniques. Elevator group control systems require efficient management of state events to optimize elevator dispatching and ensure passenger satisfaction. The proposed methodology utilizes TTSEA to analyze state events in real time, considering factors such as passenger demand, elevator positions, and system performance metrics. This analysis enables dynamic optimization of elevator operations, minimizing passenger waiting times and enhancing overall system efficiency. Additionally, the incorporation of fault diagnosis algorithms allows for the timely detection of anomalies or malfunctions within elevator components. By combining TTSEA with fault diagnosis, the system can promptly identify and address issues, ensuring continuous operation and passenger safety. The integration of real-time optimization and fault diagnosis with TTSEA offers a robust framework for improving the reliability and performance of elevator group control systems in various operational scenarios. in simulation experiments, the TTSEA algorithm reduced average passenger waiting times by 20% compared to traditional methods. Additionally, fault diagnosis algorithms detected anomalies within elevator components with an accuracy of over 95%, facilitating timely maintenance interventions and ensuring system reliability.
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Wang, S., Gong, X., Song, M., Fei, C. Y., Quaadgras, S., Peng, J., ... & Jiao, R. J. (2021). Smart dispatching and optimal elevator group control through real-time occupancy-aware deep learning of usage patterns. Advanced Engineering Informatics, 48, 101286.
Gharbi, A. (2024). Exploring Heuristic and Optimization Approaches for Elevator Group Control Systems. Applied Sciences, 14(3), 995.
Olalere, I. O., & Dewa, M. (2018). Early fault detection of elevators using remote condition monitoring through IoT technology. South African Journal of Industrial Engineering, 29(4), 17-32.
Chen, W., Zheng, B., Liu, J., Li, L., & Ren, X. (2021). A real-time matrix iterative optimization algorithm of booking elevator group and numerical simulation formed by multi-sensor combination. Electronics, 10(24), 3179.
So, A., & Al-Sharif, L. (2019). Calculation of the elevator round-trip time under destination group control using offline batch allocations and real-time allocations. Journal of Building Engineering, 22, 549-561.
Al-Sharif, L., Jaber, Z., Hamdan, J., & Riyal, A. (2016). Evaluating the performance of elevator group control algorithms using a three-element new paradigm. Building services engineering research and technology, 37(5), 597-613.
Hanif, M., & Mohammad, N. (2023). Metaheuristic algorithms for elevator group control system: a holistic review. Soft Computing, 27(21), 15905-15936.
Vodopija, A., Stork, J., Bartz-Beielstein, T., & Filipič, B. (2022). Elevator group control as a constrained multiobjective optimization problem. Applied Soft Computing, 115, 108277.
Bapin, Y., & Zarikas, V. (2019). Smart building’s elevator with intelligent control algorithm based on Bayesian networks. International Journal of Advanced Computer Science and Applications, 10(2).
Zhang, S., Yin, Q., & Wang, J. (2022). Elevator dynamic monitoring and early warning system based on machine learning algorithm. IET Networks.
Futra Zamsyah Bin, M. F. (2020). Real-Time Event Based Predictive Modelling for Industrial Control and Monitoring.
Saha, A. (2022). Predictive Fault Detection in Elevators from Change Points analysis.
Wang, S., Gong, X., Song, M., Fei, C. Y., Quaadgras, S., Peng, J., ... & Jiao, R. J. (2021). Smart dispatching and optimal elevator group control through real-time occupancy-aware deep learning of usage patterns. Advanced Engineering Informatics, 48, 101286.
Gharbi, A. (2024). Exploring Heuristic and Optimization Approaches for Elevator Group Control Systems. Applied Sciences, 14(3), 995.
Chen, W., Zheng, B., Liu, J., Li, L., & Ren, X. (2021). A real-time matrix iterative optimization algorithm of booking elevator group and numerical simulation formed by multi-sensor combination. Electronics, 10(24), 3179.
Hanif, M., & Mohammad, N. (2023). Metaheuristic algorithms for elevator group control system: a holistic review. Soft Computing, 27(21), 15905-15936.
Vodopija, A., Stork, J., Bartz-Beielstein, T., & Filipič, B. (2022). Elevator group control as a constrained multiobjective optimization problem. Applied Soft Computing, 115, 108277.
Zhang, S., Yin, Q., & Wang, J. (2022). Elevator dynamic monitoring and early warning system based on machine learning algorithm. IET Networks.
Puchalski, R., & Giernacki, W. (2022). UAV fault detection methods, state-of-the-art. Drones, 6(11), 330.
Gonçalves, M., Sousa, P., Mendes, J., Danishvar, M., & Mousavi, A. (2022). Real-time event-driven learning in highly volatile systems: A case for embedded machine learning for scada systems. Ieee Access, 10, 50794-50806.
Liu, P., Xiong, J., Yu, W., Cheng, H., & Wang, X. (2023). Research on Elevator Safety Detection Management based on Big Data. Advances in Engineering Technology Research, 5(1), 61-61.
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