A physics-informed deep learning approach for bearing fault detection
A physics-informed deep learning approach for bearing fault detection
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DOI：
10.1016/j.engappai.2021.104295
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发表时间：
2021-05-18
影响因子：
8
通讯作者：
Kenny, Shawn
中科院分区：
文献类型：
--
作者：
Shen, Sheng;Lu, Hao;Kenny, Shawn
近年来，计算机技术的进步和大数据的出现使深度学习在轴承状态监测和故障检测方面取得了令人瞩目的成功。虽然现有的深度学习方法能够有效地检测和分类轴承故障，但这些方法中的大多数完全依赖于数据，并且没有将物理知识纳入学习和预测过程-或者更重要的是，将轴承故障的物理知识嵌入模型训练过程中，这使得模型具有物理意义。为了应对这一挑战，我们提出了一种基于物理的深度学习方法，该方法由一个简单的阈值模型和一个用于轴承故障检测的深度卷积神经网络（CNN）模型组成。在所提出的物理信息深度学习方法中，阈值模型首先基于轴承故障的已知物理特性评估轴承的健康类别。然后，CNN模型自动从输入数据中提取高级特征，并充分利用这些特征来预测轴承的健康等级。我们设计了一个用于训练和验证CNN模型的损失函数，当将这些知识嵌入CNN模型时，它选择性地放大阈值模型吸收的物理知识的效果。使用（1）来自田间作业的农业机器上的18个轴承的数据，以及（2）来自凯斯西储大学（CWRU）轴承数据中心实验室测试台上的轴承数据，验证了所提出的物理信息深度学习方法。
In recent years, advances in computer technology and the emergence of big data have enabled deep learning to achieve impressive successes in bearing condition monitoring and fault detection. While existing deep learning approaches are able to efficiently detect and classify bearing faults, most of these approaches depend exclusively on data and do not incorporate physical knowledge into the learning and prediction processes-or more importantly, embed the physical knowledge of bearing faults into the model training process, which makes the model physically meaningful. To address this challenge, we propose a physics-informed deep learning approach that consists of a simple threshold model and a deep convolutional neural network (CNN) model for bearing fault detection. In the proposed physics-informed deep learning approach, the threshold model first assesses the health classes of bearings based on known physics of bearing faults. Then, the CNN model automatically extracts high-level characteristic features from the input data and makes full use of these features to predict the health class of a bearing. We designed a loss function for training and validating the CNN model that selectively amplifies the effect of the physical knowledge assimilated by the threshold model when embedding this knowledge into the CNN model. The proposed physics-informed deep learning approach was validated using (1) data from 18 bearings on an agricultural machine operating in the field, and (2) data from bearings on a laboratory test stand in the Case Western Reserve University (CWRU) Bearing Data Center.