Interpretable physics-informed domain adaptation paradigm for cross-machine transfer diagnosis

He, Chao; Shi, Hongmei; Liu, Xiaorong; Li, Jianbo

doi:10.1016/j.knosys.2024.111499

Cited by 11 publications

(3 citation statements)

References 48 publications

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“…In this section, the vibration data of roller bearings at JNU [35][36][37] tested analyzed to verify the effectiveness of the proposed method. Figure 25 shows the experiment setup for the bearing fault diagnosis.…”

Section: Bearing With Outer Racementioning

confidence: 99%

An improved envelope spectrum via Hoyer index-gram for bearing fault extraction

Tian,

Tang,

Wang

et al. 2024

Meas. Sci. Technol.

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Resonance demodulation is one of the most commonly used methods in rolling bearing fault diagnosis, yet determining the optimal demodulation band has been a significant challenge. The vibration signal from a faulty bearing may include not only periodic fault impulses but also discrete harmonic interferences, random impulses, Gaussian white noise, among others. To enhance fault information and attenuate the impact of interference signals, this paper proposes an Improved Envelope Spectrum via Hoyer Index-gram (IESHoyergram). By utilizing the Hoyer index of the spectrum-related enhanced envelope spectrum as the frequency band filtering criterion, the proposed method extracts periodic impulses while suppressing interference from random impulses and other sources. Moreover, owing to the multilevel segmentation based on the different trend components in the spectral correlation spectrogram, IESHoyergram avoids the shortcomings of traditional segmentation methods. The proposed method is validated through both simulated and experimentally acquired data, demonstrating its capability not only to enhance the characteristics of a single fault but also to separate multiple component faults.

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Section: Bearing With Outer Racementioning

confidence: 99%

An improved envelope spectrum via Hoyer index-gram for bearing fault extraction

Tian,

Tang,

Wang

et al. 2024

Meas. Sci. Technol.

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“…For a higher prediction efficiency, the characteristics of the signal, especially the timing characteristic, need to be considered [12][13][14][15]. The timing characteristics of the signal have been considered in finance analysis [16,17], fault diagnosis [18][19][20], traffic prediction [21] etc.…”

Section: Introductionmentioning

confidence: 99%

Drill tools sticking prediction based on adaptive long short-term memory

Wu,

Wang,

et al. 2024

Meas. Sci. Technol.

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As one of the most severe disasters in deep coal mining, rockburst can be prevented through drill-hole pressure relief. However, the coal mine is characterized by high crustal stress and changeable mechanical properties of surrounding rock, which will cause drill rod deflection phenomenon, then lead to rod-deflection sticking accidents. This paper proposes a prediction method based on adaptive long short-term memory (ALSTM) for rod-deflection sticking accidents to improve drilling efficiency and reduce sticking accidents. Firstly, the sticking data is obtained through the intelligent drilling condition simulation experimental platform, and then the sticking features are extracted based on the sticking data. Secondly, the sticking factor is constructed, and the sticking critical line is set. Thirdly, the good-point set and the proposed random perturbation algorithm are employed to improve the spotted hyena optimizer (SHO) to obtain the improved spotted hyena optimizer (ISHO). Finally, we use the ISHO to optimize the hyperparameters of the long short-term memory and then establish the sticking prediction model based on ALSTM. The experimental results show that the proposed prediction model meets the demands for sticking prediction very well.

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“…The timely diagnosis of rolling bearing faults is significant for equipment safety and stability [ 6 , 7 ]. However, early fault features are weak and easily obscured by noise [ 8 , 9 ]. Complex working conditions, such as speed fluctuation and load, add challenges to fault diagnosis [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions

Dong,

Zheng,

Wen

et al. 2024

Sensors

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Recent advancements in applications of deep neural network for bearing fault diagnosis under variable operating conditions have shown promising outcomes. However, these approaches are limited in practical applications due to the complexity of neural networks, which require substantial computational resources, thereby hindering the advancement of automated diagnostic tools. To overcome these limitations, this study introduces a new fault diagnosis framework that incorporates a tri-channel preprocessing module for multidimensional feature extraction, coupled with an innovative diagnostic architecture known as the Lightweight Ghost Enhanced Feature Attention Network (GEFA-Net). This system is adept at identifying rolling bearing faults across diverse operational conditions. The FFE module utilizes advanced techniques such as Fast Fourier Transform (FFT), Frequency Weighted Energy Operator (FWEO), and Signal Envelope Analysis to refine signal processing in complex environments. Concurrently, GEFA-Net employs the Ghost Module and the Efficient Pyramid Squared Attention (EPSA) mechanism, which enhances feature representation and generates additional feature maps through linear operations, thereby reducing computational demands. This methodology not only significantly lowers the parameter count of the model, promoting a more streamlined architectural framework, but also improves diagnostic speed. Additionally, the model exhibits enhanced diagnostic accuracy in challenging conditions through the effective synthesis of local and global data contexts. Experimental validation using datasets from the University of Ottawa and our dataset confirms that the framework not only achieves superior diagnostic accuracy but also reduces computational complexity and accelerates detection processes. These findings highlight the robustness of the framework for bearing fault diagnosis under varying operational conditions, showcasing its broad applicational potential in industrial settings. The parameter count was decreased by 63.74% compared to MobileVit, and the recorded diagnostic accuracies were 98.53% and 99.98% for the respective datasets.

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Interpretable physics-informed domain adaptation paradigm for cross-machine transfer diagnosis

Cited by 11 publications

References 48 publications

An improved envelope spectrum via Hoyer index-gram for bearing fault extraction

An improved envelope spectrum via Hoyer index-gram for bearing fault extraction

Drill tools sticking prediction based on adaptive long short-term memory

Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions

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