Deep learning-based research has drawn much attention in the field of fault diagnosis of various mechanical systems due to its powerful performance. In deep learning-based methods, signals become the input for a deep learning algorithm. However, the performance of an algorithm can be diminished if there is significant noise in the data. To address the noise issue, this paper proposes a fault diagnosis method called multi-task learning of classification and denoising (MLCD). The proposed method is designed to make a fault diagnosis algorithm robust against the noise in vibration signals by learning the denoising task simultaneously with the classification. Given a noisy input, MLCD can improve test accuracy by implementing denoising as an auxiliary task, using hyperparameters chosen by Bayesian optimization. To validate the proposed MLCD method, it is integrated with the two most commonly used deep learning algorithms: long short-term memory and one-dimensional convolutional neural network. For a case study, these algorithms are tested to classify the states of a rotor testbed data. The results show that the proposed MLCD method extracts noise-robust and meaningful features; ultimately, this improves the fault diagnosis performance.

基于深度学习的研究由于其强大的性能而在各种机械系统的故障诊断领域引起了很多关注。在基于深度学习的方法中，信号成为深度学习算法的输入。但是，如果数据中存在大量噪声，则算法的性能可能会降低。为了解决噪声问题，本文提出了一种故障诊断方法，称为分类和去噪多任务学习（MLCD）。通过与分类同时学习降噪任务，该方法旨在使故障诊断算法对振动信号中的噪声具有鲁棒性。给定有噪声的输入，MLCD可以通过使用贝叶斯优化选择的超参数将去噪作为辅助任务来实现，从而提高测试精度。为了验证提出的MLCD方法，它与两种最常用的深度学习算法集成：长短期记忆和一维卷积神经网络。对于案例研究，测试了这些算法以对转子测试台数据的状态进行分类。结果表明，所提出的MLCD方法提取出了鲁棒性强且有意义的特征。最终，这提高了故障诊断性能。