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Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images

机译：主动磁轴承－转子系统的振动图像故障诊断

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摘要

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.
机译：作为旋转机械故障诊断的重要来源，振动信号通常在时域或频域中被处理为区分不同类别故障的特征。然而，这些类型的处理方法总是忽略多个信号之间的对应关系，从而导致信息丢失。本文提出了一种新的故障描述策略，称为振动图像，在此基础上提取了三种新的特征，其中包含不同振动信号通道之间的耦合信息。此外，还设计了一种称为两层AdaBoost的新特征融合方法来训练故障识别模型，当数据集不够大时可以避免过拟合。基于振动图像的特征结合两层AdaBoost来诊断旋转机械的故障。以主动磁轴承-转子系统为实验平台，收集了具有四类故障的数据集，该算法取得了较好的性能。同时，基于振动图像的特征和两层AdaBoost都被证明是有效的。

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