Search Results (2)

The monorail tourism transit system (MTTS) is a large-scale amusement facility. Currently, there is limited theoretical research on the vehicle–bridge coupling vibration and dynamic amplification factor (DAFs) of this system. The values specified in relevant standards are not entirely reasonable; for instance, the calculated value of the DAFs in the “Large-scale amusement device safety code (GB 8408-2018)” only takes speed into account and is set at 0.44 when the speed is between 20 and 40 km/h. This is overly simplistic and obviously too large. This paper aims to establish a reasonable expression of the DAFs for the MTTS and improve the design code of the industry. Firstly, using on-site trials of the project and the dynamics numerical simulation method, the dynamic response characteristics of the MTTS and the influencing factors of the DAFs were systematically analyzed. The rationality and accuracy of the model were verified. Secondly, combined with the joint simulation model, the dynamic influence mechanism of multifactor coupling on the DAFs was revealed. On this basis, the key regression parameters were selected by using the Pearson correlation coefficient method and the random forest algorithm, and the DAFs prediction model was constructed based on the least absolute shrinkage and selection operator (LASSO) regression theory. Finally, through cross-comparison of simulation data and specification verification, a recommended calculation expression of the DAFs for the MTTS was proposed. The research results show that the established prediction model can predict 94.50% of the variation information of the DAFs of the MTTS and pass the 95% confidence level and 0.05 significance test. The accuracy is high and relatively reasonable and can provide a reference for the design of the MTTS. Full article

The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors’ expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults. Full article