Fault Diagnosis of Mine Ventilator Bearing Based on Improved Variational Mode Decomposition and Density Peak Clustering

Zhang, Xi; Wang, Hongju; Li, Xuehui; Gao, Shoujun; Guo, Kui; Wei, Yingle

doi:10.3390/machines11010027

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Applying the improved sparrow search algorithm mentioned in the reference literature [28] for parameter optimization is a valuable approach. This algorithm can assist in finding optimal or nearoptimal parameter settings by simulating the search behavior of sparrows and their interactions within an optimization space.…”

Section: Resultsmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and SSA-LSTM

Wang,

Zhang,

Ren

et al. 2023

Preprint

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The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

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Section: Resultsmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and SSA-LSTM

Wang,

Zhang,

Ren

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Applying the improved sparrow search algorithm mentioned in the reference literature [52] for parameter optimization is a valuable approach. This algorithm can assist in finding optimal or near-optimal parameter settings by simulating the search behavior of sparrows and their interactions within an optimization space.…”

Section: Resultsmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM

Wang,

Zhang,

Ren

et al. 2023

Entropy

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The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In the real operational life of bearings, fault information often gets submerged within the noise. Furthermore, employing Long Short-Term Memory (LSTM) neural networks for time series prediction necessitates the configuration of appropriate parameters. Manual parameter selection is often a time-consuming process and demands substantial prior knowledge. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. The improved sparrow search algorithm (ISSA) is employed for configuring parameters in the Long Short-Term Memory (LSTM) network. Each technique’s principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

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“…Primary mine fans are essential pieces of equipment in mine ventilation systems, and they are key pieces of mechanical equipment that guarantee safe coal production [ 1 , 2 ]. A primary mine fan provides sufficient fresh air and regulates the temperature and humidity of the underground air at each operating location.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings in Primary Mine Fans under Sample Imbalance Conditions

Cui,

Ding,

Meng

et al. 2023

Entropy

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Rolling bearings are crucial parts of primary mine fans. In order to guarantee the safety of coal mine production, primary mine fans commonly work during regular operation and are immediately shut down for repair in case of failure. This causes the sample imbalance phenomenon in fault diagnosis (FD), i.e., there are many more normal state samples than faulty ones, seriously affecting the precision of FD. Therefore, the current study presents an FD approach for the rolling bearings of primary mine fans under sample imbalance conditions via symmetrized dot pattern (SDP) images, denoising diffusion probabilistic models (DDPMs), the image generation method, and a convolutional neural network (CNN). First, the 1D bearing vibration signal was transformed into an SDP image with significant characteristics, and the DDPM was employed to create a generated image with similar feature distributions to the real fault image of the minority class. Then, the generated images were supplemented into the imbalanced dataset for data augmentation to balance the minority class samples with the majority ones. Finally, a CNN was utilized as a fault diagnosis model to identify and detect the rolling bearings’ operating conditions. In order to assess the efficiency of the presented method, experiments were performed using the regular rolling bearing dataset and primary mine fan rolling bearing data under actual operating situations. The experimental results indicate that the presented method can more efficiently fit the real image samples’ feature distribution and generate image samples with higher similarity than other commonly used methods. Moreover, the diagnostic precision of the FD model can be effectively enhanced by gradually expanding and enhancing the unbalanced dataset.

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Fault Diagnosis of Mine Ventilator Bearing Based on Improved Variational Mode Decomposition and Density Peak Clustering

Cited by 5 publications

References 23 publications

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and SSA-LSTM

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and SSA-LSTM

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM

Fault Diagnosis of Rolling Bearings in Primary Mine Fans under Sample Imbalance Conditions

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