Xi’an Jiaotong University
The National Natural Science Foundation of China (Key Program, 61633001)
针对传统稀疏滤波网络缺乏多尺度特征提取能力, 难以充分挖掘故障信息的问题, 提出一种多尺度稀疏滤波网络. 该网络包括五层: 多尺度粗粒度层获取多尺度信号; 样本分段层对每个尺度的信号分段; 局部特征提取层计算每个片段的特征向量; 特征平均化层将单个尺度下所有片段的特征向量池化以得到输入信号在该尺度下的表征; 特征堆叠层将所有尺度下的表征堆叠成一个长向量, 作为输入信号最终的特征向量. 采集三个齿轮数据集进行实验验证, 可视化和聚类结果表明多尺度网络从齿轮振动信号中提取的特征比原始网络提取的特征更具区分性和判别性. 用Softmax对这两种网络及三种传统多尺度方法提取的特征进行分类, 结果显示, 多尺度稀疏滤波网络对每种故障的识别精度均最高. 同时, 本文多尺度稀疏滤波网络的性能与两种其它框架下的多尺度网络相比非常有竞争力. 所提出的多尺度稀疏滤波网络可广泛用于机械故障诊断的特征提取阶段, 能自动从大量无标注样本中挖掘有用的故障信息.
Traditional sparse filtering network (SFN) lacks multi-scale feature extraction ability, which makes it fail to dig adequate fault information. To deal with this problem, we propose multi-scale sparse filtering network (MSSFN) which includes five layers. In particular, the multi-scale coarse-grained layer aims at obtaining multi-scale signals. The sample segmentation layer plays the role of dividing each sample at each scale into several segments. The local feature extraction layer aims to calculate feature vector of each segment. The feature averaging layer targets at averaging all segments as the feature representation of the input signal under this scale. The feature stacking layer plays the role of stacking all feature representations at different scales into a long vector as the final feature vector of the input signal. Three gear datasets are collected to validate the effectiveness. The results about visualization and clustering show that MSSFN is able to learn more discriminative features from the gear vibration signals than those learned by SFN. Softmax is used to classify features extracted by these two types of networks as well as three traditional multi-scale approaches, and it presents that MSSFN achieves the highest recognition accuracy for each type of the gear fault. At the same time, the proposed MSSFN achieves the very competitive diagnosis results, in comparison with two other types of multi-scale networks under different architectures. The proposed MSSFN can be widely applied to the stage of feature extraction for machinery fault diagnosis, where it can discover useful fault information from massive unlabelled samples automatically.