The invention discloses an escalator fault diagnosis method based on multi-sensor data input. The escalator fault diagnosis method comprises the four steps of escalator original data collection, data preprocessing, fault data set setting, escalator fault recognition and cloud model updating. According to the data preprocessing, escalator signal data are filtered through an improved wavelet threshold noise reduction method; the quality of escalator signal data is improved; the fault data set is set by fusing fault data of all parts and all types of the escalator, full utilization of the fault data of the escalator is achieved, and the richness of fault category signals is improved. According to fault identification, multi-sensor data is input into a CNN-GRU network, and the CNN-GRU network extracts features in the input data and obtains a final identification result. According to cloud model updating, the CNN-GRU model is optimized by using CFOA, a network model with the optimal CNN-GRU is constructed, the recognition precision is high, the generalization performance is good, and the method can be widely applied to the field of escalator fault diagnosis.
本发明公开了一种基于多传感器数据输入的电扶梯故障诊断方法,包括电扶梯原始数据的采集、数据预处理、故障数据集设置、电扶梯故障识别与云端模型更新四个步骤:数据预处理使用改进小波阈值降噪方法对电扶梯信号数据进行滤波,提高电扶梯信号数据的质量;故障数据集设置融合电扶梯各部位以及各类型的故障数据来设置数据集,实现了电扶梯故障数据的充分利用,提高了故障类别信号的丰富度;故障识别将多传感器数据输入CNN‑GRU网络,由CNN‑GRU网络提取输入数据中的特征并得出最终的识别结果;云端模型更新利用使用CFOA对CNN‑GRU模型进行寻优,构建具有最优CNN‑GRU网络模型,具有较高的识别精度,且泛化性能良好,可广泛应用于电扶梯故障诊断领域。
Escalator fault diagnosis method based on multi-sensor data input
一种基于多传感器数据输入的电扶梯故障诊断方法
2025-03-14
Patent
Electronic Resource
Chinese
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