Bearing fault diagnosis using time segmented Fourier synchrosqueezed transform images and convolution neural network

Gundewar, Swapnil K.; Kane, Prasad V.

doi:10.1016/j.measurement.2022.111855

Cited by 37 publications

(13 citation statements)

References 56 publications

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“…In the first stream, the vibration signal is directly transformed into the spectrogram image of size n × n by the constant-Q transform (CQT). The CQT has been widely applied in many deep learning methods in the task of bearing fault diagnosis in previous studies [33][34][35][36][37][38][39].…”

Section: Data Preprocessingmentioning

confidence: 99%

Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature

et al. 2023

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In this work, we propose a deep-learning method to diagnose bearing faults of electric motors based on vibration and bearing housing temperature. Our methods can accurately diagnose faults related to bearing cracking and lubricant shortages. The proposed method is effective in terms of computational complexity and model capacity thanks to the advantages of neural architecture search (NAS) and parameter quantization in the model establishment. The experimental results found that the information on bearing temperature improved the diagnostic accuracy for the bearing fault diagnosis task. The proposed method has explored the most optimal model in terms of computational resources and model capacity with a pre-defined accuracy target. The searched model has a relatively high diagnostic accuracy of 98.7% and a size of about 27.3 kB. After quantization, the obtained model maintained 96.9% accuracy and reduced 4 times in size. All experiments are executed elaborately on our custom bearing fault dataset.

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Section: Data Preprocessingmentioning

confidence: 99%

Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature

et al. 2023

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“…Commonly used techniques include utilizing mean, median, and Gaussian filtering [18]. Frequency domain filtering commonly involves using Fourier transform and wavelet transform to convert images into the frequency domain for filtering operations [19,20]. Model-based methods demand prior knowledge to establish mathematical models and are mainly used for image restoration, deblurring, and super-resolution reconstruction [21].…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on the feature enhancement of improved mean differential SDP images★

Wang,

Sun

2024

Eng. Res. Express

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In the fault diagnosis of mechanical equipment, vibrational signals often contain noise. In order to solve the problem of rolling bearing fault diagnosis under the influence of noise, an image enhancement method based on mean difference images is proposed. Convert one-dimensional time series into two-dimensional image data by using the symmetrized dot pattern method. The data are processed and grouped by variational mode decomposition to obtain a mean image with stable features. A feature enhancement method based on improved mean difference images is used to achieve data mining of fault features. The improved Canberra distance is used as the classification basis to realize the accurate classification of rolling bearing faults. Finally, the classification effect of the method is verified by experiments. The experimental results show that the image enhancement method proposed in this paper can improve fault features in the image and has a good anti-noise ability.

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“…Finally, an adaptive time-varying filter is designed using estimated IF as the center frequency, and is applied for decomposing vibration signal into several independent components. A simulation and mechanical system high speed fluctuation experiment is to validate the proposed method, and compared with IF recognition result and signal decomposition result by using Hilbert-Huang transform (HHT), 23 continuous wavelet transform (CWT), 24 synchrosqueezing wavelet transform (SWT), 24 variational mode decomposition (VMD), 25 empirical mode decomposition (EMD) 26 respectively, the proposed method has a better effectiveness and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Mechanical varying non-stationary signal separation method based on instantaneous frequency estimation

Yan

Qin-Xiao

Zhong

et al. 2023

Advances in Mechanical Engineering

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Mechanical equipment often works on variable speed condition, its corresponding vibration signal presents multi-component, modulation coupling with fast time-varying instantaneous frequency (IF), how to effectively compute IF and realize fasting varying non-stationary signal decoupling separation plays an important role in mechanical system fault diagnosis. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to identify, extract, and trend IF for achieving a highly concentrated time-frequency energy. Simulation demonstrates that the proposed method performs better than traditional IF estimation method. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 10%.

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Bearing fault diagnosis using time segmented Fourier synchrosqueezed transform images and convolution neural network

Cited by 37 publications

References 56 publications

Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature

Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature

Bearing fault diagnosis based on the feature enhancement of improved mean differential SDP images★

Mechanical varying non-stationary signal separation method based on instantaneous frequency estimation

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