The invention discloses an elevator health condition assessment method based on a fusion network model, and belongs to the technical field of special equipment assessment. The method comprises the steps that multi-dimensional data of a to-be-tested elevator are collected and preprocessed; constructing a BP neural network model, taking a genetic algorithm GA as a strategy for optimizing a hyper-parameter, a weight and a threshold value of the BP neural network model to obtain a GA-BP neural network model, and obtaining a first evaluation result of the elevator health condition based on the GA-BP neural network model; a logic judgment model is constructed, and a second evaluation result of the elevator health condition is obtained; and fusing and grading evaluation results of the GA-BP neural network model and the logic judgment model, and taking a grading result as a final elevator health condition diagnosis result. According to the method, the learning and prediction capabilities of the neural network and the intuition and accuracy of logical judgment are combined, so that the accuracy and efficiency of elevator health condition evaluation are improved, and a powerful guarantee is provided for safe operation of the elevator.
本发明公开了一种基于融合网络模型的电梯健康状况评估方法,属于特种设备评估技术领域。该方法包括:采集待测电梯的多维数据并进行预处理;构建BP神经网络模型,将遗传算法GA作为优化BP神经网络模型超参数、权值与阈值的策略,得到GA‑BP神经网络模型,基于GA‑BP神经网络模型得到电梯健康状况的第一评估结果;构建逻辑判断模型,得到电梯健康状况的第二评估结果;将GA‑BP神经网络模型以及逻辑判断模型的评估结果进行融合并分级,将分级结果作为最终的电梯健康状况诊断结果。本发明结合了神经网络的学习和预测能力以及逻辑判断的直观性和精确性,以提高电梯健康状况评估的准确性和效率为电梯的安全运行提供有力保障。
Elevator health condition assessment method based on fusion network model
一种基于融合网络模型的电梯健康状况评估方法
25.02.2025
Patent
Elektronische Ressource
Chinesisch
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