Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu, Yong; Li, Guangpeng; Wang, Rui; Tang, Shengnan; Hong, Soon-Kwang; Cao, Kai

doi:10.3390/s21020549

Cited by 43 publications

(38 citation statements)

References 42 publications

(50 reference statements)

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“…A more comprehensive comparison between 1DCNN and MC1DCNN has been made in the results of this paper, so the 1DCNN model of the literature [ 25 , 26 ] is not compared subsequently. The models selected for comparison are the VMD-ELM model of the literature [ 15 ], the MVMD-SVM model of the literature [ 17 ], and the VMD-CNN model of the literature [ 22 ]. Since both the VMD-ELM model and the MVMD-SVM model require feature extraction of the vibration signal, in the process of performing MWPE feature extraction, we found that the larger embedding dimension and scale factor of the Multiscale Weighted Permutation Entropy (MWPE) algorithm increase the computing time significantly, so the CNN model has an absolute advantage in diagnostic time after training is completed.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…On the other hand, vibration data can be transformed into image formats such as grayscale images, frequency domain maps and speech spectrum maps for recognition. In [ 22 ], Zhu transformed the signal by short-time Fourier transform into a frequency domain map for fault diagnosis by CNN. In [ 23 ], Zhao transformed the one-dimensional vibration signal into a two-dimensional grayscale image and achieved diagnostic classification of faults by CNN.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Zhao

Sun

Lin

et al. 2021

Sensors

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Rolling mill multi-row bearings are subjected to axial loads, which cause damage of rolling elements and cages, so the axial vibration signal contains rich fault character information. The vertical shock caused by the failure is weakened because multiple rows of bearings are subjected to radial forces together. Considering the special characters of rolling mill bearing vibration signals, a fault diagnosis method combining Adaptive Multivariate Variational Mode Decomposition (AMVMD) and Multi-channel One-dimensional Convolution Neural Network (MC1DCNN) is proposed to improve the diagnosis accuracy. Additionally, Deep Convolutional Generative Adversarial Network (DCGAN) is embedded in models to solve the problem of fault data scarcity. DCGAN is used to generate AMVMD reconstruction data to supplement the unbalanced dataset, and the MC1DCNN model is trained by the dataset to diagnose the real data. The proposed method is compared with a variety of diagnostic models, and the experimental results show that the method can effectively improve the diagnosis accuracy of rolling mill multi-row bearing under unbalanced dataset conditions. It is an important guide to the current problem of insufficient data and low diagnosis accuracy faced in the fault diagnosis of multi-row bearings such as rolling mills.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Zhao

Sun

Lin

et al. 2021

Sensors

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show abstract

“…In the orthogonal experiment analysis, although the range analysis is simple, the effect of the experimental error on the results cannot be excluded, and the accuracy of the analysis cannot be verified. Therefore, the F function should be used to conduct variance analysis for each factor and the interaction between factors, further excluding the experimental error and exploring the effect of each factor on the index [40,41]. The F function is as follows:…”

Section: Variance Analysis Of the Orthogonal Experimentsmentioning

confidence: 99%

Design and Optimization of High-Pressure Water Jet for Coal Breaking and Punching Nozzle Considering Structural Parameter Interaction

et al. 2022

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The technology of increasing coal seam permeability by high-pressure water jet has significant advantages in preventing and controlling gas disasters in low-permeability coal seam. The structural parameters of a nozzle are the key to its jet performance. The majority of the current studies take strike velocity as the evaluation index, and the influence of the interaction between the nozzle’s structural parameters on its jet performance is not fully considered. In practice, strike velocity and strike area will affect gas release in the process of coal breaking and punching. To further optimize the structural parameters of coal breaking and punching nozzle, and improve water jet performance, some crucial parameters such as the contraction angle, outlet divergence angle, and length-to-diameter ratio are selected. Meanwhile, the maximum X-axis velocity and effective Y-axis extension distance are used as evaluation indexes. The effect of each key factor on the water jet performance is analyzed by numerical simulation using the single factor method. The significance and importance effect of each factor and their interaction on the water jet performance are quantitatively analyzed using the orthogonal experiment method. Moreover, three optimal combinations are selected for experimental verification. Results show that with an increase in contraction angle, outlet divergence angle, and length-to-diameter ratio, the maximum X-axis velocity increases initially and decreases thereafter. The Y-direction expansion distance of the jet will be improved significantly with an increase in the outlet divergence angle. Through field experiments, the jet performance of the improved nozzle 3 is the best. After optimization, the coal breaking and punching diameter of the nozzle is increased by 118%, and the punching depth is increased by 17.46%.

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“…According to the literature [37], approximately 20% of all damages in hydraulic systems are pump damages, which are the key elements of the system. Due to the importance of this element, more effective methods of diagnosing them are in the interests of many researchers in the world [38][39][40]. One of the most popular types of hydraulic pumps is a variable displacement axial piston pump.…”

Section: Introductionmentioning

confidence: 99%

Wear Analysis of Additively Manufactured Slipper-Retainer in the Axial Piston Pump

Klimek¹,

Kluczyński²,

Łuszczek³

et al. 2022

Preprint

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The paper contains the results of the 100-hour test campaign of the Additive Manufactured (AM) spare part (retainer – slipper) dedicated for an exact axial piston pump. The material of the retainer-slipper has been identified by using energy dispersive spectroscopy and replaced by other – with similar material properties as the original one. The obtained spare part had been subjected to only one postprocessing type (sandblasting) to analyze the influence of the rough part after the AM process. The whole test campaign has been divided into stages, where after each stage microscopic measurements have been made. During microscopical investigation roughness and geometrical measurements were conducted. The results revealed that it is possible to replace parts in hydraulic pumps with the use of AM. 100-hour test campaign caused about 500% increase in the surface roughness of the pump’s original part which was cooperated with the AM spare retainer-slipper, without any damages to the test system.

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Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Cited by 43 publications

References 42 publications

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Design and Optimization of High-Pressure Water Jet for Coal Breaking and Punching Nozzle Considering Structural Parameter Interaction

Wear Analysis of Additively Manufactured Slipper-Retainer in the Axial Piston Pump

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