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A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation

机译：基于无监督域适应的机器智能故障诊断的一种新方法

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摘要

Data driven fault diagnosis has attracted a lot of attention in recent years owing to its intelligent and accurate detection of fault categories. However, it is a challenge for its applications in real world. The abundant labeled data is extremely necessary for data driven fault diagnosis to train a favorable model. Even though enough labeled data is prepared for training a model, we still cannot ensure the data used for training and testing draw from identical distribution. In other words, the labeled source domain has different distribution compared with the unlabeled target domain. In this paper, we introduce the domain adaptation strategy into deep neural networks to propose a deep domain adaptation architecture, which realizes to learn knowledge from the labeled source domain to facilitate the target classification. In the proposed model, the conditional and marginal distribution are adapted together in multiple layers of neural network, which uses MMD to measure the distribution discrepancy. Besides, the relative importance between marginal and conditional distributions is explored, and an adaptively weighted strategy is further introduced to learn the relative importance of the two distributions. To evaluate the proposed method, we conduct the simulations on different workloads, sensor deployment locations, and even different platforms. The results show the superiority of the proposed model to other intelligent fault diagnosis methods, meanwhile verify the necessity of marginal and conditional distribution adaptation and adaptive weighted strategy. (c) 2020 Elsevier B.V. All rights reserved.
机译：由于其智能和准确检测故障类别，数据驱动的故障诊断近年来引起了很多关注。但是，它对现实世界中的应用是一项挑战。数据驱动的故障诊断是培训有利模型的丰富标记数据。即使准备训练模型的足够标记的数据，我们仍然无法确保用于培训和测试从相同的分布中的数据。换句话说，与未标记的目标域相比，标记的源域具有不同的分布。在本文中，我们将域适应策略介绍到深度神经网络中，以提出深度域适应架构，这实现了从标记的源域中学习知识，以便于目标分类。在所提出的模型中，条件和边缘分布在多层神经网络中调整在一起，它使用MMD来测量分布差异。此外，探索了边缘和条件分布之间的相对重要性，进一步引入了自适应加权战略，以了解两个分布的相对重要性。为了评估所提出的方法，我们在不同的工作负载，传感器部署位置甚至不同平台上进行模拟。结果表明，所提出的模型对其他智能故障诊断方法的优势，同时验证了边缘和条件分布适应和自适应加权策略的必要性。 （c）2020 Elsevier B.v.保留所有权利。

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