本文最后更新于:8 小时前

更新日期:2019年8月21日
此页为PHM相关的文献汇总,主要关注机器学习在其中的应用

一、综述文献

文献 描述 日期
Hoang, Duy-Tang, and Hee-Jun Kang. “A survey on Deep Learning based bearing fault diagnosis.“ Neurocomputing (2018). 此文重点关注自编码器、限制性玻尔兹曼机、卷积神经网络在轴承故障诊断中的应用 2018.6
Liu R , Yang B , Zio E , et al. Artificial intelligence for fault diagnosis of rotating machinery: A review[J]. Mechanical Systems and Signal Processing, 2018, 108:33-47. 本文重点关注人工智能算法在旋转机械故障诊断中的应用,这些算法包括k近邻、朴素贝叶斯、支持向量机、人工神经网络、深度学习等 2018.2
Khan S , Yairi T . A review on the application of deep learning in system health management[J]. Mechanical Systems and Signal Processing, 2018, 107(1):241-265. 这篇文章系统地总结了人工智能在PHM中的应用,尤其是深度学习在这领域的应用。 2017.11
Rui Z , Ruqiang Y , Zhenghua C , et al. Deep learning and its applications to machine health monitoring[J]. Mechanical Systems and Signal Processing, 2019, 115:213-237. 重点介绍深度学习算法如自编码器、限制性玻尔兹曼机、深度信念网络、深度玻尔兹曼机、卷积神经网络和循环神经网络等在设备健康监测方面的应用 2016.12
Smith W A , Randall R B . Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64-65:100-131. 本文使用三种方法对CWRU数据集做了研究与分析 2015.4

二、故障诊断

1、卷积神经网络方法

文献 描述 日期
Zhu Z, Peng G, Chen Y, et al. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis[J]. Neurocomputing, 2019, 323: 62-75. 本文使用了胶囊网络结构,输入为二维频域图,最后有三个输出分支:分类故障类型、回归损坏程度和重构输入 2018.9
Liu H, Zhou J, Xu Y, et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks[J]. Neurocomputing, 2018, 315: 412-424. 本文提出了Categorical Adversarial Autoencoder (CatAAE)结构,使用GAN的思想做无监督故障诊断 2018.8
Huang, Ruyi, et al. “Deep decoupling convolutional neural network for intelligent compound fault diagnosis.IEEE Access7 (2018): 1848-1858. 2018.7
Abdeljaber O , Avci O , Kiranyaz M S , et al. 1-D CNNs for Structural Damage Detection: Verification on a Structural Health Monitoring Benchmark Data[J]. Neurocomputing, 2017:S0925231217315886. 训练CNN需要大量的测量,尤其在CNN结构比较大的时候。本文提出一种基于CNN的加强的方法,只需测量两侧,不论结构多大。 2017.9
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox 此文用频域信号作为输入,与原始信号、频域谱信号、时域-频域信号输入作对比。同时与从时域、频域、小波域提出的人工特征做对比。 2017.7
Zhang W , Li C , Peng G , et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems and Signal Processing, 2018, 100:439-453. 2017.6
Lu C, Wang Z, Zhou B. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification[J]. Advanced Engineering Informatics, 2017, 32: 139-151. 构造特征输入矩阵,使用贪婪方法训练CNN 2017.2
Guo X, Chen L, Shen C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis[J]. Measurement, 2016, 93: 490-502. 层次学习率自适应深度CNN(ADCNN),可以选择合适的学习率。层次结构:第一层次分类故障,第二层次估计故障大小 2016.7
Ince T, Kiranyaz S, Eren L, et al. Real-time motor fault detection by 1-D convolutional neural networks[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7067-7075. 使用原始信号作为CNN的输入 2016.5
Lee D, Siu V, Cruz R, et al. Convolutional neural net and bearing fault analysis[C]//Proceedings of the International Conference on Data Mining (DMIN). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016: 194. 使用原始信号作为CNN的输入 2016.
Chen Z Q, Li C, Sanchez R V. Gearbox fault identification and classification with convolutional neural networks[J]. Shock and Vibration, 2015, 2015. 本文使用多种特征提出方式,将特征reshape成2D结构,输入到CNN中 2015.4

2、玻尔兹曼机、深度信念网络、多层感知机、自编码机、无监督方法

文献 描述 日期
Chen Z , Deng S , Chen X , et al. Deep neural networks-based rolling bearing fault diagnosis[J]. Microelectronics Reliability, 2017:S0026271417300513. 本文使用三种深度神经网络:深度玻尔兹曼机、深度信念网络、堆叠自编码机,来做轴承故障诊断

3、循环神经网络方法

文献 描述
Lei J , Liu C , Jiang D . Fault diagnosis of wind turbine based on Long Short-Term memory networks[J]. Renewable Energy, 2018. 本文提出了一种使用LSTM的端到端的故障诊断框架

4、迁移学习

文献 描述 日期
Wang Q , Michau G , Fink O . Domain Adaptive Transfer Learning for Fault Diagnosis[J]. 2019. 2019.5
Siyu S , Stephen M A , Ruqiang Y , et al. Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning[J]. IEEE Transactions on Industrial Informatics, 2018:1-1. 2019.4
Yang, B., Lei, Y., Jia, F., & Xing, S. (2019). An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. *Mechanical Systems and Signal Processing, 122*, 692-706. 2018.12
Guo, L., Lei, Y., Xing, S., Yan, T., & Li, N. (2018). Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, 66(9), 7316-7325. 2018.9
Han T , Liu C , Yang W , et al. Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application[J]. 2018. 2018.4
Cao P , Zhang S , Tang J . Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning[J]. 2017. 2017.10
Wen L , Gao L , Li X . A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017:1-9. 2017.9
Zhang B , Li W , Tong Z , et al. Bearing fault diagnosis under varying working condition based on domain adaptation[J]. 2017. 2017.7
Zhang R , Tao H , Wu L , et al. Transfer Learning with Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions[J]. IEEE Access, 2017:1-1. 2017.6
Wei Z , Gaoliang P , Chuanhao L , et al. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals[J]. Sensors, 2017, 17(2):425-. 本文提出一种名为Deep Convolutional Neural Networks with Wide First-layer Kernels(WDCNN)的结构,使用AdaBN来做领域自适应 2017.2
Lu W , Liang B , Cheng Y , et al. Deep Model Based Domain Adaptation for Fault Diagnosis[J]. IEEE Transactions on Industrial Electronics, 2016, PP(99):1-1. 2016.10
Wang J , Xie J , Zhang L , et al. A factor analysis based transfer learning method for gearbox diagnosis under various operating conditions[C]// 2016 International Symposium on Flexible Automation (ISFA). IEEE, 2016. 本文使用因子分析的方法寻找不同域之间的关键特征,通过将特征隐射到低维隐空间,选择出使域之间差别最小的特征,再将这些特征输入到分类器 2016.8

三、RUL预测

1、卷积神经网络方法

文献 描述
Li X, Ding Q, Sun J Q. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering & System Safety, 2018, 172: 1-11. 本文使用CNN做端到端的RUL预测。

2、循环神经网络方法

文献 描述
Ellefsen A L, Bjørlykhaug E, Æsøy V, et al. Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture[J]. Reliability Engineering & System Safety, 2019, 183: 240-251. 本文使用玻尔兹曼机和LSTM两种网络结构,做半监督的RUL预测,同时使用遗传算法调参。

四、硕博论文

文献 描述 学位
石鑫. 基于深度学习的变压器故障诊断技术研究[D]. 2018. 使用限制波尔兹曼机与经 BP 微调之后的网络对分类能力进行研究,分析了隐层节点数、学习率与迭代次数对特征提取能力的影响;研究了基于 DBN 方法进行故障诊断的应用过程,对不同长度划分样本下的数据集的计算能力进行研究,通过参数寻优的方法改善手动调节参数没有依据的问题 硕士
江国乾. 基于排序模式分析与深度学习的风电设备故障诊断方法研究[D]. 2017. 以风电轴承为研究对象,使用排序模式分析方法提出排序信息散度指标,量化分析系统当前运行状态与健康参考状态间振动信号在高维相空间中排序模式概率分布的差异性。以风电齿轮箱对研究对象,使用基于堆叠去噪自编码器的频域故障特征自学习和诊断方法。针对风电齿轮箱时域振动信号的固有多尺度特性及传统卷积神经网络特征提取能力的不足,提出一种新的多尺度卷积神经网络结构。针对风电机组 SCADA 数据的非线性与多变量时空相关性,构建一种新的风电系统多变量故障检测模型:滑动窗去噪自编码器 博士

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