The growth of deep learning in the past decade has motivated important
applications to smart manufacturing and machine health monitoring. In
particular, vibration data offers a rich and reliable source to provide
meaningful insights into machine health and predictive maintenance. In this
work, we present a Transformer based framework for analyzing vibration signals
to predict different types of bearing faults (FaultFormer). In particular, we
process signal data using data augmentations and extract their Fourier modes to
train a transformer encoder to achieve state of the art accuracies. The
attention mechanism as well as model outputs were analyzed to confirm the
transformer's ability to automatically extract features within signals and
learn both global and local relationships to make classifications. Lastly, two
pretraining strategies were proposed to pave the way for large, generalizable
transformers that could adapt to new data, situations, or machinery on the
production floor.

在此工作中，我们提出了一种基于 Transformer 的框架（FaultFormer），用于分析振动信号以预测不同类型的轴承故障。通过使用数据增强和提取傅里叶模态的方法，我们训练了一个 Transformer 编码器，以实现最先进的准确度，并分析了注意机制和模型输出，以确认 Transformer 自动提取信号特征和学习全局和局部关系以进行分类的能力。最后，我们提出了两种预训练策略，为大型可推广 Transformer 的开发铺平了道路，使其能够适应生产现场的新数据、情况或机械设备。

FaultFormer：基于 Transformer 的轴承故障预测

FaultFormer: Transformer-based Prediction of Bearing Faults

Since 2006, deep learning (DL) has become a rapidly growing research
direction, redefining state-of-the-art performances in a wide range of areas
such as object recognition, image segmentation, speech recognition and machine
translation. In modern manufacturing systems, data-driven machine health
monitoring is gaining in popularity due to the widespread deployment of
low-cost sensors and their connection to the Internet. Meanwhile, deep learning
provides useful tools for processing and analyzing these big machinery data.
The main purpose of this paper is to review and summarize the emerging research
work of deep learning on machine health monitoring. After the brief
introduction of deep learning techniques, the applications of deep learning in
machine health monitoring systems are reviewed mainly from the following
aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and
its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines
(DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
Finally, some new trends of DL-based machine health monitoring methods are
discussed.

本研究指出深度学习可以为数据驱动的机器健康监测提供有用的工具，在介绍深度学习技术后，主要从自编码器、深度置信网络、卷积神经网络和循环神经网络等方面综述了其在机器健康监测系统中的应用，并讨论了一些新的基于深度学习的方法趋势。