With the huge success of applying deep learning (DL) methodologies to image recognition and natural language processing in recent years, researchers are now keen to use them in the machine condition monitoring (MCM) context. There are numerous papers in applying various DL techniques, such as auto-encoder, restricted Boltzmann machine, convolutional neural network and recurrent neural network, etc., to MCM problems ranging from component-level condition monitoring (machine tool wear prediction, bearing fault diagnosis and classification and hydraulic pump fault diagnosis) to system-level health management (aircraft and spacecraft diagnosis). In this paper, we give a brief overview in the area of DL for MCM with a focus on reviewing the most recent papers published since 2019. In Part 1, we present some critical views regarding whether any breakthrough has been achieved from an MCM domain expert perspective, with the main conclusion that DL has great potential for MCM applications, and a major breakthrough could come soon since the shortfalls lie more in data than in the DL methodologies. Our overall impression is that (a) DL models are not really showing their great potentials with only a small training data; (b) faulty-condition data is hard to come by for training DL, but normal condition data is abundant, so anomaly detection makes more sense; (c) applying DL only to the Case Western Reserve University (CWRU) bearing fault dataset is not sufficient for real world industrial applications as it was from a very simple test rig, and applying DL to data from complex systems like helicopter gearbox data may deliver much more convincing results. In Part 2, we enhance the main conclusion of the critical review with supplement views and a case study on analysing Bell-206B helicopter main gearbox planet bearing failure data using some traditional MCM techniques in contrast to applying the long short-term memory (LSTM) DL method. We can conclude from the case study that the DL-based methods are not necessarily always superior to the traditional MCM techniques for dataset from moderately complex machinery.

近年来，随着将深度学习（DL）方法应用于图像识别和自然语言处理的巨大成功，研究人员现在热衷于在机器状态监视（MCM）上下文中使用它们。关于自动编码器，受限玻尔兹曼机，卷积神经网络和递归神经网络等各种DL技术应用于MCM问题的论文很多，这些问题涉及组件级状态监测（机床磨损预测，轴承故障诊断）以及分类和液压泵故障诊断）到系统级的健康管理（飞机和航天器诊断）。在本文中，我们对MCM的DL领域进行了简要概述，重点是回顾了自2019年以来发布的最新论文。在第1部分中，我们从MCM领域专家的角度提出了关于是否取得任何突破的一些关键观点，主要结论是DL对于MCM应用具有巨大的潜力，并且主要的突破可能很快就会出现，因为数据不足比DL造成的不足更多方法论。我们的总体印象是：（a）DL模型仅通过少量训练数据并没有真正显示出其巨大潜力；（b）训练DL很难获得故障状态数据，但是正常状态数据丰富，因此异常检测更有意义；（c）仅将DL应用于Case Western Reserve University（CWRU）轴承故障数据集就不足以用于现实世界的工业应用，因为这仅来自于非常简单的测试台，并将DL应用于来自诸如直升机变速箱数据之类的复杂系统的数据可能会提供令人信服的结果。在第2部分中，我们通过补充性意见和使用一些传统MCM技术（与应用长期短期记忆（LSTM）相比）对Bell-206B直升机主变速箱行星轴承故障数据进行分析的案例研究，增强了严格审查的主要结论。 DL方法。我们可以从案例研究中得出结论，对于中等复杂机器的数据集，基于DL的方法不一定总是优于传统的MCM技术。我们使用补充的观点和使用一些传统MCM技术（与应用长期短期记忆（LSTM）DL方法相比）分析Bell-206B直升机主变速箱行星轴承故障数据的案例研究，增强了严格审查的主要结论。我们可以从案例研究中得出结论，对于中等复杂机器的数据集，基于DL的方法不一定总是优于传统的MCM技术。我们使用补充的观点和使用一些传统MCM技术（与应用长期短期记忆（LSTM）DL方法相比）分析Bell-206B直升机主变速箱行星轴承故障数据的案例研究，增强了严格审查的主要结论。我们可以从案例研究中得出结论，对于中等复杂机器的数据集，基于DL的方法不一定总是优于传统的MCM技术。