The invention provides an axle counting fault analysis method, a storage medium and electronic equipment, and belongs to the technical field of rail transit. The axle counting fault analysis method comprises the following steps: acquiring axle counting waveform data of a vehicle at a plurality of historical moments; based on the axle counting waveform data of the vehicle at the multiple historical moments, a machine learning model is adopted for modeling, and an axle counting data prediction model is obtained; predicting an axle counting fault by adopting the axle counting data prediction model to obtain a fault prediction result; wherein the axle counting data prediction model is used for predicting the variation trend of the axle counting waveform data along with time. According to the method, the axle counting fault can be effectively predicted in advance, the abnormity of the axle counting waveform data can be found in advance, the function of predicting the axle counting fault in advance is achieved, meanwhile, prediction is conducted through the machine learning model, and compared with manual analysis, the fault analysis efficiency is greatly improved.
本发明提供一种计轴故障分析方法、存储介质及电子设备,属于轨道交通技术领域。所述计轴故障分析方法包括:获取车辆在多个历史时刻的计轴波形数据;基于所述车辆在多个历史时刻的计轴波形数据,采用机器学习模型进行建模,得到计轴数据预测模型;采用所述计轴数据预测模型对计轴故障进行预测,得到故障预测结果;其中,所述计轴数据预测模型用于预测计轴波形数据随时间的变化趋势。可以提前对计轴故障进行有效的预测,从而可以提前发现计轴波形数据异常,以实现提前预测计轴故障的功能,同时,通过机器学习模型进行预测,相比于人工分析,大大提高了故障分析效率。
Axle counting fault analysis method, storage medium and electronic equipment
计轴故障分析方法、存储介质及电子设备
2024-06-21
Patent
Electronic Resource
Chinese
IPC: | B61L Leiten des Eisenbahnverkehrs , GUIDING RAILWAY TRAFFIC |
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