Luo Jiayu, Yang Xubin, Dai Qingyou, Qiu Weikun, Nie Siyu, Wu Junjun, Zeng Min
South China Academy of Advanced Optoelectronics, South China Normal University, No. 378, Waihuan West Road, Panyu District, Guangzhou 510006, China.
Guangdong Institute of Special Equipment Inspection and Research Foshan Branch, No.2, Yingyin 2 Street, Chancheng District, Foshan 528012, China.
Sensors (Basel). 2025 Sep 5;25(17):5550. doi: 10.3390/s25175550.
As a high-frequency and essential type of special electromechanical equipment, a vertical elevator has a significant societal implication for their safe operation. The load-weighing module, serving as the core component for overload warning, is susceptible to precision degradation due to the nonlinear deformation of rubber buffers installed at the base of the elevator car. This deformation arises from the coupled effects of environmental factors such as temperature, humidity, and material aging, leading to potential safety risks including missed overload alarms and false empty status detections. To address the issue of accuracy deterioration in elevator load-weighing systems, this study proposes an online self-calibration method based on multimodal information fusion. A reference detection model is first constructed to map the relationship between applied load and the corresponding relative compression of the rubber buffers. Subsequently, displacement data from a draw-wire sensor are integrated with target detection model outputs, enabling real-time extraction of dynamic rubber buffers' deformation characteristics under empty conditions. Based on the above, a displacement-based compensation term is derived to enhance the accuracy of load estimation. This is further supported by a dynamic error compensation mechanism and an online computation framework, allowing the system to self-calibrate without manual intervention. The proposed approach eliminates the dependency on manual tuning inherent in traditional methods and forms a highly robust solution for load monitoring. Field experiments demonstrate the effectiveness of the proposed method and the stability of the prototype system. The results confirm that the synergistic integration of multimodal perception and adaptive calibration technologies effectively resolves the challenge of load-weighing precision degradation under complex operating conditions, offering a novel technical paradigm for elevator safety monitoring.
作为一种高频且重要的特种机电设备，垂直电梯的安全运行具有重大的社会意义。称重模块作为过载预警的核心部件，由于安装在电梯轿厢底部的橡胶缓冲器的非线性变形，容易出现精度下降的情况。这种变形是由温度、湿度和材料老化等环境因素的耦合作用引起的，会导致潜在的安全风险，包括过载报警遗漏和空轿厢状态误检测。为了解决电梯称重系统精度下降的问题，本研究提出了一种基于多模态信息融合的在线自校准方法。首先构建一个参考检测模型，以映射施加的载荷与橡胶缓冲器相应相对压缩量之间的关系。随后，将拉线传感器的位移数据与目标检测模型的输出相结合，从而能够实时提取空载条件下动态橡胶缓冲器的变形特征。基于上述内容，推导出一个基于位移的补偿项，以提高载荷估计的精度。这进一步得到了动态误差补偿机制和在线计算框架的支持，使系统能够在无需人工干预的情况下进行自校准。所提出的方法消除了传统方法中对人工调谐的依赖，并形成了一种用于载荷监测的高度鲁棒的解决方案。现场实验证明了所提方法的有效性和原型系统的稳定性。结果证实，多模态感知与自适应校准技术的协同集成有效地解决了复杂运行条件下称重精度下降的挑战，为电梯安全监测提供了一种新颖的技术范式。
