Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

Wu, Q. H.; Zhu, Minghui

doi:10.1155/2022/7924982

Cited by 3 publications

(2 citation statements)

References 21 publications

(18 reference statements)

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“…Therefore, it is of great engineering significance to improve the crack detection capability of bearing rings and promote their quality [2]. At present, the defect detection methods for bearing rings include magnetic particle testing [3,4], eddy current testing [5,6], magnetic flux leakage (MFL) testing, ultrasonic testing [7,8], and machine vision testing [9][10][11]. MFL testing is widely used to evaluate various ferromagnetic materials.…”

Section: Introductionmentioning

confidence: 99%

Influence of curvature radius on the axial crack signal of the magnetic flux leakage detection for tapered roller bearing rings

Yang

Peng²,

Qiu³

et al. 2023

Front. Phys.

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Tapered roller bearings are widely used in heavy machinery, railway transportation, aviation, and other fields. Their quality and reliability are related to the operational safety of mechanical equipment. In the axial crack magnetic flux leakage (MFL) detection of the bearing ring, the MFL signals obtained by the sensor from different curvature radius of the surface are inconsistent, affecting the detection accuracy of cracks and subsequent quantitative analysis. In order to address the above problems, the finite element simulation is performed to analyze the influence of the surface curvature radius of the bearing ring on the magnetic field distribution inside the workpiece and the MFL signal in the circumferential magnetization. Through the parallel magnetic circuit, the difference in curvature radius is identified as the basic reason for non-uniform magnetization. On this basis, the compensation method based on the normalization of surface magnetization is proposed. Furthermore, the effectiveness of the compensation method is verified by experiments. The relative change in the amplitude of the crack MFL signal is reduced from 30% to 5%.

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Section: Introductionmentioning

confidence: 99%

Influence of curvature radius on the axial crack signal of the magnetic flux leakage detection for tapered roller bearing rings

Yang

Peng²,

Qiu³

et al. 2023

Front. Phys.

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“…However, these defect detection systems widely adopt conventional image processing methods, such as threshold segmentation, edge detection, and template matching, to analyze the acquired image. Wu and Zhu [10] combined image processing and pattern recognition methods to detect inner ring defects in bearings. Wang et al [11] determined the defective bearing based on grayscale histogram.…”

Section: Introductionmentioning

confidence: 99%

A novel CNN ensemble framework for bearing surface defects classification based on transfer learning

Ma¹,

Liu²,

Chen³

et al. 2022

Meas. Sci. Technol.

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Bearing surface defect detection and classification methods based on machine vision have been widely used in bearing quality inspection due to their high-speed, high-precision, and non-contact advantages. However, traditional machine vision algorithms have low reusability and their development processes are often expensive and time consuming. Several deep learning-based bearing surface defect detection methods have been proposed. However, these deep learning models often require a large number of datasets, which is often difficult to achieve in the actual industry. Transfer learning provides a promising solution to the small sample difficulties associated with deep learning. However, the complexity of the illumination conditions and the huge differences between bearing dataset and ImageNet dataset make it impossible to use the current single model-based transfer learning for bearing defect detection. In this study, we propose a novel transitive transfer learning convolutional neural network (CNN) ensemble framework for classifying bearing surface defects. Only small-scale datasets are needed in this framework. A transfer path and transfer method selection strategy for transitive transfer learning is then proposed to train the deep learning models, which enhances the feature extraction ability of the CNN models on the basis of multiple illuminations. Ablation experiments are conducted to verify the effectiveness of the proposed method. Experimental results show that the proposed transitive transfer learning CNN ensemble framework has the accuracy rate of 97.51%. The average time for detecting each bearing is 155 ms, which can meet the requirements of industrial online detection.

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Retracted: Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

Sensors

2023

Journal of Sensors

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Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

Cited by 3 publications

References 21 publications

Influence of curvature radius on the axial crack signal of the magnetic flux leakage detection for tapered roller bearing rings

Influence of curvature radius on the axial crack signal of the magnetic flux leakage detection for tapered roller bearing rings

A novel CNN ensemble framework for bearing surface defects classification based on transfer learning

Retracted: Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

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