The Use of Acoustic Emission Elastic Waves for Diagnosing High Pressure Mud Pumps Used on Drilling Rigs

Bejger, A.; Piasecki, Tomasz

doi:10.3390/en13051138

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…Therefore, accurate identification and classification of acoustic emission monitoring signal data are crucial [8]. Compared to traditional identification and classification methods used previously, researchers have applied many new techniques, such as machine learning, to the recognition and classification of acoustic emission signals, yielding promising results [9]. However, challenges persist, including low success rates in identification and classification, as well as low efficiency [10].…”

Section: Introductionmentioning

confidence: 99%

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

Xie,

Yang

2024

Applied Sciences

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To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals. This method leverages correlation coefficient filtering to extract the primary components, followed by classification and recognition using the Swin Transformer neural network. The results demonstrate that the improved CELMD method effectively extracts the main features of the acoustic emission signals with higher decomposition accuracy and reduced occurrences of mode mixing and end effects. Furthermore, the Swin Transformer neural network exhibits outstanding performance in classifying acoustic emission signals, surpassing both convolutional neural networks and ViT neural networks in terms of accuracy and convergence speed. Moreover, utilizing preprocessed data from the improved CELMD enhances the performance of the Swin Transformer neural network. With an increase in data volume, the accuracy, stability, and convergence speed of the Swin Transformer neural network continuously improve, and using preprocessed data from the enhanced CELMD yields superior training results compared to those obtained without preprocessing.

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Section: Introductionmentioning

confidence: 99%

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

Xie,

Yang

2024

Applied Sciences

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“…Some researchers also realized the fault diagnosis of drilling pump from the traditional mechanical learning method [5][6][7], but these diagnosis methods have the problems of insufficient fault classification and few types of diagnosis. And some scholars also used nondestructive testing technology to realize pump fault diagnosis [8][9][10]. Kumar et al [11] proposed noise subtraction, marginal enhanced square envelope spectrum, and a marginal band selection indicator criterion for spectral frequency.…”

Section: Introductionmentioning

confidence: 99%

SO-IMCKD processed signal improving MSCNN model’s fault diagnosis accuracy for drilling pump fluid end

Ding

et al. 2023

Meas. Sci. Technol.

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Drilling pump is the “heart” of drilling construction. The key to accurate fault diagnosis is to extract useful fault features from noisy raw signals. In order to improve the accuracy of fault diagnosis of drilling pump fluid end, this paper proposes a fault diagnosis method based on multi-scale convolutional neural network (MSCNN) combined with the snake optimization optimized maximum correlation kurtosis deconvolution (SO-IMCKD). First, the SO algorithm is employed to optimize the filter length and the shift number of IMCKD to process the raw signal, enhancing the fault features from the raw signal. Second, the continuous wavelet transform (CWT) is used to convert the enhanced signals into time-frequency images which are input into an established MSCNN to extract the fault feature more effectively. Finally, by changing the training batchsize of the MSCNN model, the identification effect of the model to the normal state, minor damage, and serious damage of the fluid end is analyzed. The identification of nine states of the fluid end is successfully carried out, with an average diagnostic accuracy of 99.93%. Moreover, the adaptability of the proposed method is verified with the Mechanical Failure Prevention Technology Association (MFPT) dataset. The method has high accuracy and good adaptability, which has desired prospect for drilling pump fault diagnosis and bearing fault diagnosis.

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“…Buildings 2023, 13, 2770 2 of 15 However, with the continuous expansion of the construction scale and the increasingly complex geological conditions, the demand for mud pumps has steadily increased [4][5][6]. Therefore, ensuring normal operation under more complex geological conditions, enhancing discharge efficiency to adapt to larger-scale construction, and reducing energy consumption are all pressing issues that need to be solved for the mud pump.…”

Section: Introductionmentioning

confidence: 99%

Thermo–Solid Coupling Analysis of Bionic Piston for a Mud Pump in Tunnel Engineering

Tian,

et al. 2023

Buildings

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With the development of mud shield tunnel construction technology, the demands on the working performance of a mud pump are becoming higher and higher. As one of the critical components of a mud pump that is easy to wear, the performance of the piston directly affects the operational efficiency and lifespan of the mud pump. The bionic shape of the piston was designed under the guidance of non-smooth surface characteristics of natural organisms to enhance friction and wear performance as well as longevity. The stress field and temperature field characteristics of the pistons with three bionic structures (pit, stripe, and prismatic) were analyzed based on finite element simulation. The stress field analysis results indicated that, for the prismatic shape and pit shape pistons, the maximum stress was concentrated in the lip regions, and both of them bore large stress at the root. For the stripe-shaped piston, the stress was dispersed on both sides of the stripe structure, the stress at the root was small, and the stress gradient along the axial direction was relatively gentle. The stripe-shaped bionic structure can significantly improve the stress distribution state on the piston surface, and the optimal stripe width was recommended to be between 1 and 1.5 mm. The temperature field analysis results indicated that, for the stripe-shaped piston, the surface temperature and heat flux were the smallest, and the temperature gradient was relatively smaller than that of pit-shaped and prismatic-shaped pistons, so it was easier to dissipate heat. When the stripe width was 1.5 mm, the temperature distribution was the most uniform, and the heat flux in localized areas was the smallest, so the heat generated by friction was relatively easy to discharge in the unit area.

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The Use of Acoustic Emission Elastic Waves for Diagnosing High Pressure Mud Pumps Used on Drilling Rigs

Cited by 11 publications

References 12 publications

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

SO-IMCKD processed signal improving MSCNN model’s fault diagnosis accuracy for drilling pump fluid end

Thermo–Solid Coupling Analysis of Bionic Piston for a Mud Pump in Tunnel Engineering

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