Deep Reinforcement Learning-Based Online Domain Adaptation Method for Fault Diagnosis of Rotating Machinery

2021 ◽  
pp. 1-10
Author(s):  
Guoqiang Li ◽  
Jun Wu ◽  
Chao Deng ◽  
Xuebing Xu ◽  
Xinyu Shao
Keyword(s):  
Reinforcement Learning ◽  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rotating Machinery ◽  
Adaptation Method

scholarly journals Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaofan Hu ◽  
Zhichao Zhou ◽  
Biao Wang ◽  
WeiGuang Zheng ◽  
Shuilong He
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Subspace Learning ◽  
Rotating Machinery ◽  
Training Data ◽  
Tensor Representation ◽  
Vibration Signals ◽  
Tensor Representations ◽  
Partial Label Learning ◽  
And Training

A new tensor transfer approach is proposed for rotating machinery intelligent fault diagnosis with semisupervised partial label learning in this paper. Firstly, the vibration signals are constructed as a three-way tensor via trial, condition, and channel. Secondly, for adapting the source and target domains tensor representations directly, without vectorization, the domain adaptation (DA) approach named tensor-aligned invariant subspace learning (TAISL) is first proposed for tensor representation when testing and training data are drawn from different distribution. Then, semisupervised partial label learning (SSPLL) is first introduced for tackling a problem that it is hard to label a large number of instances and there exists much data left to be unlabeled. Ultimately, the proposed method is used to identify faults. The effectiveness and feasibility of the proposed method has been thoroughly validated by transfer fault experiments. The experimental results show that the presented technique can achieve better performance.

scholarly journals Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 319 ◽  
pp. 03001
Author(s):  
Weigui Li ◽  
Zhuqing Yuan ◽  
Wenyu Sun ◽  
Yongpan Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Transfer Problem ◽  
Intelligent Manufacturing ◽  
Maximum Mean Discrepancy ◽  
Bearing Fault Diagnosis ◽  
Cross Domain ◽  
Conditional Maximum ◽  
Adaptation Method

Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.

scholarly journals Multi-Layer domain adaptation method for rolling bearing fault diagnosis

2019 ◽  
Vol 157 ◽  
pp. 180-197 ◽  
Author(s):  
Xiang Li ◽  
Wei Zhang ◽  
Qian Ding ◽  
Jian-Qiao Sun
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Adaptation Method

Time-frequency synthesis analysis for complex signal of rotating machinery via variational mode manifold reinforcement learning

2019 ◽  
Vol 234 (7) ◽  
pp. 1438-1455
Author(s):  
Quanchang Li ◽  
Xiaoxi Ding ◽  
Tao Wang ◽  
Mingkai Zhang ◽  
Wenbin Huang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Fault Diagnosis ◽  
Amplitude Distribution ◽  
Rotating Machinery ◽  
Transient Signal ◽  
Complex Signal ◽  
Full Time ◽  
Frequency Synthesis ◽  
Complex Signals ◽  
Time Frequency

The transient signal caused by localized fault in rotating machinery always contains complex modulation information with heavy background noise distributed, which brings much difficulties for fault feature identification in the application of rotating machinery fault diagnosis. Focusing on the sensitive feature extraction from these complex signals, this paper proposes a novel variational mode manifold reinforcement learning (VM2RL) to adaptively construct time-frequency synthesis analysis for enhancement of transient features. First, a method of adaptive variational mode decomposition (VMD) is employed to divide the raw spectrum of the given signal into several sub-bands with different frequency modulation information. Second, an improved time-frequency manifold (ITFM) learning is introduced to gain the topological manifold structure from those sub-distributions in time-frequency domain. Then, a sound-enhanced signature of transient features on the whole time-frequency plane can be synthesized by combining those sub-TFMs from each modulated segment back to the corresponding frequency band. Finally, the time-frequency envelope spectrum for fault diagnosis is further obtained through statistically evaluating their amplitude distribution. Among them, short-frequency Fourier transform (SFFT) is introduced to transform local frequency bands into a series of TFDs which improves the computational efficiency of TFM learning. In this manner, the desired transient distribution on full time-frequency plane can be automatically reconstructed by VM2RL with manifold reinforced in a data-driven way. A simulation study and two experimental signals are both analyzed here, and fast spectral kurtosis and conventional VMD methods are also used to verify its effectiveness. Meanwhile, a quantitative analysis has been provided to further illustrate its superiority in the application of complex signal fault of rotating machinery.

Sign in / Sign up

Export Citation Format

Share Document