基于信息融合的稀疏自编码故障诊断

Sparse Auto-Coding Fault Diagnosis Based on Information Fusion

针对目前大多数机械故障诊断中单一振动加速度信号特征提取对先验知识要求高和对时域、频域信息利用不充分等问题,提出了一种基于信息融合的稀疏自编码故障诊断方法。首先,对振动加速度信号进行频域积分得到速度和位移信号,同时计算加速度信号的频谱;其次,将频谱、速度、位移三种信号融合成一个复合信号;最后,将复合信号作为稀疏自编码网络的输入进行深度特征提取,利用SoftMax分类器进行状态识别。通过调整不同比例的输入信息来调整模型,并与传统的稀疏自编码故障诊断模型相比,结果表明,所提方法能有效识别滚动轴承故障和RV行星轮故障,且在减少网络层数的同时能够提高识别准确率。

In order to solve the problems of high prior knowledge requirement and insufficient information utilization in time domain and frequency domain in feature extraction of single vibration acceleration signal in most mechanical fault diagnosis,a sparse self-coding fault diagnosis method based on information fusion was proposed.Firstly,the velocity and displacement signals are obtained by frequency domain integration of vibration acceleration signal,and the frequency spectrum of acceleration signal is calculated.Secondly,the spectrum,velocity and displacement signals are fused into a composite signal.Finally,the composite signal is used as the input of sparse auto-coding network for depth feature extraction,and SoftMax classifier is used for state recognition.The model was adjusted by adjusting the input information of different proportions,and compared with the traditional sparse auto-coding fault diagnosis model,the results show that the proposed method can effectively identify the rolling bearing fault and RV planetary wheel fault,and can improve the recognition accuracy while reducing the number of network layers.