The invention belongs to the technical field of train fault detection, particularly relates to a railway train wheel set tread fault detection method and device and a medium, and provides a fault diagnosis algorithm for optimizing a BP neural network through a genetic algorithm on the basis of extracting wheel set tread fault vibration signal feature vectors through a wavelet packet algorithm. According to the method, a wheel set tread fault diagnosis and risk prediction model is constructed, the extracted feature vectors are used as input parameters of a neural network model, a BP neural network optimized by a genetic algorithm is adopted for training and learning, and the diagnosis efficiency and accuracy of wheel set tread faults can be greatly improved.
本申请属于列车故障检测技术领域,具体为一种铁路列车轮对踏面故障检测方法、设备及介质,在采用小波包算法提取轮对踏面故障振动信号特征向量的基础上,提出了一种遗传算法优化BP神经网络的故障诊断算法,构建了轮对踏面故障诊断与风险预测模型,将提取的特征向量作为神经网络模型的输入参数,采用遗传算法优化的BP神经网络进行训练和学习,能够大幅提高轮对踏面故障的诊断效率和准确率。
Railway train wheel set tread fault detection method and device and medium
一种铁路列车轮对踏面故障检测方法、设备及介质
2024-05-10
Patent
Electronic Resource
Chinese
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / B61L Leiten des Eisenbahnverkehrs , GUIDING RAILWAY TRAFFIC / G01H MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES , Messen von mechanischen Schwingungen oder Ultraschall-, Schall- oder Infraschallwellen / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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