基于堆栈稀疏自编码器的齿轮箱故障诊断

Fault diagnosis of gearbox based on stacked sparse autoencoder

针对当前齿轮箱故障诊断需要进行复杂的特征提取以及识别准确率不高等问题,提出了一种基于堆栈稀疏自编码器(SSAE)的齿轮箱故障诊断方法,采用时域分析对故障信号进行特征预处理,然后将其输入稀疏自编码器网络中进行特征优化以及降维,提取出表征信号本质信息的特征,最后将其输入到Softmax分类器中实现齿轮箱故障的分类。实验结果表明,该方法在相同工况和混合工况下的均能达到较高的识别精度,在混合工况下,其识别精度达到99.5%,高于文中提出的其他模型,因此该方法能有效地用于齿轮箱故障诊断。

Aiming at the complex feature extraction and low recognition accuracy of gear box fault diagnosis,a fault diagnosis method based on stack sparse self-encoder( SSAE) is proposed. The fault signal is pre-processed by time domain analysis,and then it was input into sparse self-encoder network for feature optimization and dimensionality reduction to extract tables. The feature of intrinsic information of signature signal is input into Softmax classifier to classify surface defects of strip. The experimental results show that the proposed method can achieve high recognition accuracy under the same and mixed conditions. For the mixed conditions,the recognition accuracy reaches 99.5%,which is higher than other models proposed in this paper. Therefore,the proposed method can be effectively applied to gear box fault diagnosis.