Spectral kurtosis based on evolutionary digital filter in the application of rolling element bearing fault diagnosis

Jie, Dabin; Zheng, Guodi; Zhang, Yong; Ding, Xiaoxi; Wang, Liming

doi:10.1504/ijhm.2021.114173

Cited by 15 publications

(11 citation statements)

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“…The twenty-five sets of roundness data are filtered [25,26] and imported into the simulation model for calculation, and the calculation results of UAFF (see Figure 8a-e) and CIF (see Figures 8f) on the shaft are obtained. It can be seen that for the shaft constructed based on the same data source, in general, the smaller the roundness deviation, the smaller and more stable the force on the shaft at different speeds.…”

Section: Effect Of the Roundnessmentioning

confidence: 99%

Evaluation of the Influence of Shaft Shape Errors on the Rotation Accuracy of Aerostatic Spindle—Part 1: Modeling

Zhang

Zheng

et al. 2022

Electronics

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In the research on the aerostatic spindle, for quantifying the influence of shaft shape errors on its rotation accuracy, the calculation models of unbalanced air film force (UAFF) and centrifugal inertia force (CIF) are established. Based on the typical shape of the shaft, three composite cylinder models are constructed. The data sources are selected from the cylindricity measurement results of the shaft and substituted into the model for calculation. The results show that, for the shaft in this paper, when the roundness deviation is ≤1.25 μm and the cylindricity deviation is ≤2 μm, the influence of shape errors on the rotation accuracy of the shaft is small enough. The harmonic analysis of the data source shows that the magnitude and stability of the UAFF on the shaft are determined by the actual shape of the shaft’s cylindrical surface, and cannot be inferred only by the value of shape errors.

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Section: Effect Of the Roundnessmentioning

confidence: 99%

Evaluation of the Influence of Shaft Shape Errors on the Rotation Accuracy of Aerostatic Spindle—Part 1: Modeling

Zhang

Zheng

et al. 2022

Electronics

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“…They reduced the amount of noise and blurring while increasing the contrast range to make the images more visible [ 17 ]. The existing models suffer from various issues such as overfitting [ 13 , 18 , 19 ], vanishing gradient [ 20 – 22 ], and poor convergence speed [ 23 – 25 ] kinds of problems.…”

Section: Related Workmentioning

confidence: 99%

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models

Alharbi

Aravinda²,

Meng

et al. 2022

Computational Intelligence and Neuroscience

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Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide reliable results when analyzing and recognizing the same datasets. To reduce discrepancies and improve robustness and generalization, we developed a model that analyzes blood samples to determine whether the cells are parasitized or not. Experiments were conducted on 13,750 parasitized and 13,750 parasitic samples. Support vector machines achieved 94% accuracy, XG-Boost models achieved 90% accuracy, and neural networks achieved 80% accuracy. Among these three models, the support vector machine was the most accurate at distinguishing parasitized cells from uninfected ones. An accuracy rate of 97% was achieved by the convolution neural network in recognizing the samples. The deep learning model is useful for decision making because of its better accuracy.

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“…From the above literature, it is found that the existing models suffer from hyperparameters tuning [ 1 , 24 – 26 ], gradient vanishing [ 27 , 28 ], and overfitting [ 29 – 31 ] problems. Therefore, an efficient model is required that can overcome these issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Segmentation and Classification of White Blood Cells Using the UNet

Alharbi

Aravinda²,

Meng

et al. 2022

Contrast Media & Molecular Imaging

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In the bone marrow, plasma cells are made up of B lymphocytes and are a type of WBC. These plasma cells produce antibodies that help to keep bacteria and viruses at bay, thus preventing inflammation. This presents a major challenge for segmenting blood cells, since numerous image processing methods are used before segmentation to enhance image quality. White blood cells can be analyzed by a pathologist with the aid of computer software to identify blood diseases accurately and early. This study proposes a novel model that uses the ResNet and UNet networks to extract features and then segments leukocytes from blood samples. Based on the experimental results, this model appears to perform well, which suggests it is an appropriate tool for the analysis of hematology data. By evaluating the model using three datasets consisting of three different types of WBC, a cross-validation technique was applied to assess it based on the publicly available dataset. The overall segmentation accuracy of the proposed model was around 96%, which proved that the model was better than previous approaches, such as DeepLabV3+ and ResNet-50.

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Spectral kurtosis based on evolutionary digital filter in the application of rolling element bearing fault diagnosis

Cited by 15 publications

References 0 publications

Evaluation of the Influence of Shaft Shape Errors on the Rotation Accuracy of Aerostatic Spindle—Part 1: Modeling

Evaluation of the Influence of Shaft Shape Errors on the Rotation Accuracy of Aerostatic Spindle—Part 1: Modeling

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models

Segmentation and Classification of White Blood Cells Using the UNet

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