Rotatory Machine Fault Diagnosis based on Improved One-Dimensional Residuals Convolutional Auto-encoding Network and Class Adaptation with Limited Samples

• Received:2021-06-01 Revised:2021-08-31 Published:2021-09-17

基于改进残差卷积自编码网络的类自适应旋转机械故障诊断

1. 1. Air Force Engineering University
2. 空军工程大学
3. 陕西省西安市空军工程大学防空反导学院发射系统教研部电力系统教研室
• 通讯作者: 邵思羽

Abstract: Abstract: Aiming at the insufficient model training problem caused by limited rotating machinery sensor signal samples, a fault diagnosis framework that combines an improved residual auto-encoding network and a class-adaptive method was proposed to deal with the problem of limited training samples. Firstly, paired sampled were created by a small amount of labeled source domain data and unlabeled target domain data. An improved one-dimensional residual convolutional auto-encoding network was designed to extract features from two different distributions of original vibration signals. Secondly, the maximum mean difference was used to reduce the distribution difference, and the data space of the same fault category from different domains was mapped to a common feature space, which finally helped realize accurate fault diagnosis. The experimental verification is carried out through the experimental platform data set, and the results show that the proposed fault diagnosis framework is able to effectively improve the fault diagnosis accuracy of the micro-labeled target domain vibration data under different working conditions compared with the fine-tuning and domain adaptation methods.