Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China.
Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China.
Sensors (Basel). 2019 Jan 10;19(2):244. doi: 10.3390/s19020244.
As important sources in fault diagnosis of rotary machinery, vibration signals are usually processed in the time or frequency domain as features to distinguish different classes of faults. However, these kinds of processing methods always ignore the corresponding relations among multiple signals, resulting in information loss. In this paper, a new fault description strategy named vibration image is proposed, based on which three new kinds of features are extracted, containing coupling information between different channels of vibration signals. Additionally, a new feature fusion method called two-layer AdaBoost is designed to train the fault recognition model, which avoids overfitting when the dataset is not large enough. Features based on vibration images combined with two-layer AdaBoost are adopted to diagnose faults of rotary machinery. Taking an active magnetic bearing-rotor system as the experimental platform, a dataset with four classes of faults is collected and our algorithm achieves good performance. Meanwhile, features based on vibration images and two-layer AdaBoost are both proved to be efficient separately.
作为旋转机械故障诊断的重要来源，振动信号通常在时间或频率域中进行处理，作为区分不同故障类别的特征。然而，这些处理方法往往忽略了多个信号之间的对应关系，导致信息丢失。在本文中，提出了一种新的故障描述策略，称为振动图像，基于该策略提取了三种新的特征，包含振动信号不同通道之间的耦合信息。此外，设计了一种新的特征融合方法，称为两层 AdaBoost，用于训练故障识别模型，当数据集不够大时，避免了过拟合。基于振动图像的特征与两层 AdaBoost 相结合，用于诊断旋转机械的故障。以主动磁轴承-转子系统为实验平台，采集了四类故障的数据集，我们的算法取得了良好的性能。同时，基于振动图像和两层 AdaBoost 的特征也分别被证明是有效的。
