Li Haiying, Wang Qinyang, Song Jiancheng
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control, Taiyuan University of Technology, Taiyuan 030024, China.
Sensors (Basel). 2024 Sep 27;24(19):6274. doi: 10.3390/s24196274.
This paper presents a fault diagnosis method for a vacuum contactor using the generalized Stockwell transform (GST) of vibration signals. The objective is to solve the problem of low diagnostic performance efficiency caused by the inadequate feature extraction capability and the redundant pixels in the graph background. The proposed method is based on the time-frequency graph optimization technique and ShuffleNetV2 network. Firstly, vibration signals in different states are collected and converted into GST time-frequency graphs. Secondly, multi-resolution GST time-frequency graphs are generated to cover signal characteristics in all frequency bands by adjusting the GST Gaussian window width factor . The OTSU algorithm is then combined to crop the energy concentration area, and the size of these time-frequency graphs is optimized by 68.86%. Finally, considering the advantages of the channel split and channel shuffle methods, the ShuffleNetV2 network is adopted to improve the feature learning ability and identify fault categories. In this paper, the CKJ5-400/1140 vacuum contactor is taken as the test object. The fault recognition accuracy reaches 99.74%, and the single iteration time of model training is reduced by 19.42%.
本文提出了一种基于振动信号广义斯托克韦尔变换（GST）的真空接触器故障诊断方法。目的是解决因特征提取能力不足以及图形背景中存在冗余像素而导致诊断性能效率低下的问题。所提出的方法基于时频图优化技术和ShuffleNetV2网络。首先，采集不同状态下的振动信号并将其转换为GST时频图。其次，通过调整GST高斯窗宽度因子生成多分辨率GST时频图，以覆盖所有频带的信号特征。然后结合OTSU算法裁剪能量集中区域，这些时频图的尺寸优化了68.86%。最后，考虑到通道拆分和通道混洗方法的优点，采用ShuffleNetV2网络提高特征学习能力并识别故障类别。本文以CKJ5 - 400/1140真空接触器作为测试对象。故障识别准确率达到99.74%，模型训练的单次迭代时间减少了19.42%。
