Design of Rotor-side Controller Using Adaptive Time-frequency Method for DFIG Bearing Fault Detection

2013 ◽  
Author(s):  
Suratsavadee K. Korkua
Keyword(s):  
Fault Detection ◽  
Frequency Method ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Adaptive Time

scholarly journals Deep Transfer Learning for Machine Diagnosis: From Sound and Music Recognition to Bearing Fault Detection

2021 ◽  
Vol 11 (24) ◽  
pp. 11663
Author(s):  
Eugenio Brusa ◽  
Cristiana Delprete ◽  
Luigi Gianpio Di Maggio
Keyword(s):  
Fault Detection ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Fine Tuning ◽  
Sound Recognition ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Sound Detection ◽  
Music Recognition

Today’s deep learning strategies require ever-increasing computational efforts and demand for very large amounts of labelled data. Providing such expensive resources for machine diagnosis is highly challenging. Transfer learning recently emerged as a valuable approach to address these issues. Thus, the knowledge learned by deep architectures in different scenarios can be reused for the purpose of machine diagnosis, minimizing data collecting efforts. Existing research provides evidence that networks pre-trained for image recognition can classify machine vibrations in the time-frequency domain by means of transfer learning. So far, however, there has been little discussion about the potentials included in networks pre-trained for sound recognition, which are inherently suited for time-frequency tasks. This work argues that deep architectures trained for music recognition and sound detection can perform machine diagnosis. The YAMNet convolutional network was designed to serve extremely efficient mobile applications for sound detection, and it was originally trained on millions of data extracted from YouTube clips. That framework is employed to detect bearing faults for the CWRU dataset. It is shown that transferring knowledge from sound and music recognition to bearing fault detection is successful. The maximum accuracy is achieved using a few hundred data for fine-tuning the fault diagnosis model.

scholarly journals A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6025
Author(s):  
Feiyue Deng ◽  
Chao Liu ◽  
Yongqiang Liu ◽  
Rujiang Hao
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Working Environment ◽  
Permutation Entropy ◽  
Mechanical Equipment ◽  
Noise Interference ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Self Adaptive

Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time−frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time−frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel’s subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time−frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.

Memory Residual Regression Autoencoder for Bearing Fault Detection

2021 ◽  
pp. 1-1
Author(s):  
Xin Huang ◽  
Guangrui Wen ◽  
Shuzhi Dong ◽  
Haoxuan Zhou ◽  
Zihao Lei ◽  
...  
Keyword(s):  
Fault Detection ◽  
Bearing Fault ◽  
Bearing Fault Detection
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