摘要翻译：
轴承失效是旋转机械中最常见的失效形式，会造成巨大的经济损失甚至人员伤亡。然而，轴承周围复杂的结构和实际多变的工况会导致振动信号在训练集和测试集之间存在较大的分布差异，从而造成故障诊断精度下降的问题。因此，如何有效地提高轴承在不同工况下的故障诊断性能一直是一个主要的挑战。提出了一种新的基于转移特征域自适应的轴承故障诊断方法。通过对电机在不同转速和负载条件下的原始振动信号进行快速傅立叶变换，得到正常轴承和故障轴承的数据集。然后，基于特征空间中的最大均值差异(MMD)，通过精化基于训练数据的最近邻(NN)分类器获得的伪测试标签，同时约简跨域的边缘分布和条件分布，经过多次迭代，实现训练域和测试域的鲁棒可转移特征表示。通过对可传递特征进行训练的神经网络分类器，最终准确地识别出轴承故障类别。大量实验结果表明，该方法在不同工况下均能准确识别轴承故障，性能明显优于竞争方法。
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英文标题：
《Bearing fault diagnosis based on domain adaptation using transferable
  features under different working conditions》
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作者：
Zhe Tong, Wei Li, Bo Zhang, Meng Zhang
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The dataset of normal bearing and faulty bearings are obtained through the fast Fourier transformation(FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy(MMD) in feature space by refining pseudo test labels, which can be obtained by the Nearest-Neighbor(NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.
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PDF链接：
https://arxiv.org/pdf/1806.01512