论文标题
可解释的基于学习的深度预后模型，用于旋转机械
An Explainable Deep Learning-based Prognostic Model for Rotating Machinery
论文作者
论文摘要
本文开发了一个可解释的深度学习模型，该模型估计了旋转机械的剩余使用寿命。该模型使用自动编码器从傅立叶变换中提取高级功能。这些功能被用作前馈神经网络的输入，以估计其余的使用寿命。本文通过分析特征的组成以及特征与估计结果之间的关系来解释模型的行为。为了使模型可以解释，本文引入了八度带过滤。过滤可减少自动编码器的输入大小并简化模型。案例研究证明了解释模型的方法。该研究还表明，模型中的八度带过滤模仿了低级卷积层的功能。该结果支持使用过滤来减少模型深度的有效性。
This paper develops an explainable deep learning model that estimates the remaining useful lives of rotating machinery. The model extracts high-level features from Fourier transform using an autoencoder. The features are used as input to a feedforward neural network to estimate the remaining useful lives. The paper explains the model's behavior by analyzing the composition of the features and the relationships between the features and the estimation results. In order to make the model explainable, the paper introduces octave-band filtering. The filtering reduces the input size of the autoencoder and simplifies the model. A case study demonstrates the methods to explain the model. The study also shows that the octave band-filtering in the model imitates the functionality of low-level convolutional layers. This result supports the validity of using the filtering to reduce the depth of the model.
