文章研究了一种基于深度学习的机械故障诊断方法,通过分析机械部件产生的振动信号诊断故障.系统设计包括机械部件、振动信号采集以及深度学习振动信号分析模型.在算法方面,该模型采用融合长短期记忆(Long Short-Term Memory,LSTM)和卷积神经网络(Convolutional Neural Network,CNN)的结构,有效地捕捉振动信号的时序和空间信息.在凯斯西储大学(Case Western Reserve University,CWRU)滚动轴承数据集上进行实验验证,结果表明该方法在多类别故障诊断任务中表现优秀,具有高准确性和可靠性.
Research on Mechanical Fault Diagnosis Method Based on Deep Learning
This paper investigates a deep learning-based method for mechanical fault diagnosis through the analysis of vibration signals generated by mechanical components.The system design includes mechanical components,vibration signal acquisition,and a deep learning vibration signal analysis model.The model employs a structure that combines Long Short-Term Memory(LSTM)and Convolutional Neural Networks(CNN),effectively capturing both temporal and spatial information in the vibration signals.Experimental validation on the Case Western Reserve University(CWRU)bearing dataset demonstrates the outstanding performance of the proposed method in multi-category fault diagnosis tasks,showcasing high accuracy and reliability.
deep learningmechanical failureLong Short-Term Memory(LSTM)modelConvolutional Neural Network(CNN)
万方数据
维普
