Adaptive wavelet filter based on Fractional Lower Order Moment for bearing fault diagnosis

2010 ◽  
Author(s):  
Gang Yu ◽  
Xuefeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Lower Order ◽  
Order Moment ◽  
Wavelet Filter ◽  
Adaptive Wavelet ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chun Lv ◽  
Peilin Zhang ◽  
Dinghai Wu ◽  
Bing Li ◽  
Yunqiang Zhang
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Morphological Filter ◽  
Wavelet Filter ◽  
Bearing Fault ◽  
Singular Value Decomposition Method ◽  
Bearing Fault Diagnosis ◽  
Important Means ◽  
Value Decomposition ◽  
Better Than

Bearing fault signal analysis is an important means of bearing fault diagnosis. To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology. Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF). The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.

Higher-order moment matching based fine-grained adversarial domain adaptation method for intelligent bearing fault diagnosis

2021 ◽  
Author(s):  
Rui Wang ◽  
Weiguo Huang ◽  
Juanjuan Shi ◽  
Jun Wang ◽  
Changqing Shen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Order Statistics ◽  
Domain Adaptation ◽  
Higher Order ◽  
Order Moment ◽  
Moment Matching ◽  
Bearing Fault ◽  
Fine Grained ◽  
Bearing Fault Diagnosis ◽  
Adaptation Method

Abstract Due to the data distribution discrepancy caused by the time-varying working conditions, the intelligent diagnosis methods fail to achieve accurate fault classification in engineering scenarios. To this end, this paper presents a novel higher-order moment matching-based adversarial domain adaptation method (HMMADA) for intelligent bearing fault diagnosis. First, the deep one-dimensional convolution neural network is constructed as the feature extractor to learn the discriminative features of each category through different domains. Then, the distribution discrepancy across domains is significantly reduced by using the joint higher-order moment statistics (HMS) and adversarial learning. In particular, the HMS integrates the first-order and second-order statistics into a unified framework and achieves a fine-grained distribution adaptation between different domains. Finally, the feasibility and effectiveness of the HMMADA are validated by several transfer experiments constructed on two different bearing datasets. The results demonstrate that the HMS is more effective compared with the lower-order statistics.

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