Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings

Li, Yongbo; Xu, Min; Liang, Xihui; Huang, Wenhu

doi:10.1109/tie.2017.2650873

Cited by 199 publications

(90 citation statements)

References 43 publications

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“…Although the average result is superior to the result based on single-scale morphological operators, it causes contour offset and mistakes. Some researchers improved multiscale morphological operators by introducing a weighted coefficient to (4), and they defined adaptive multiscale morphological operators as follows [28] where w j is the weighted coefficient on the jth scale result. However, because the computing of weighted coefficients is complex, the adaptive multiscale morphological operators have a low computational efficiency.…”

Section: B Multiscale and Adaptive Mathematical Morphologymentioning

confidence: 99%

Adaptive Morphological Reconstruction for Seeded Image Segmentation

Lei

Jia

Liu

et al. 2019

IEEE Trans. on Image Process.

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Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to filter seeds (regional minima) to reduce over-segmentation. However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. Firstly, AMR can adaptively filter useless seeds while preserving meaningful ones. Secondly, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that AMR is useful for improving algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time. Source code is available at https://github.com/SUST-reynole/AMR.

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Section: B Multiscale and Adaptive Mathematical Morphologymentioning

confidence: 99%

Adaptive Morphological Reconstruction for Seeded Image Segmentation

Lei

Jia

Liu

et al. 2019

IEEE Trans. on Image Process.

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“…With the progress of time, in the field of mechanical fault diagnosis, the analysis and processing of a vibrational signal is always a hot spot. 1,2 In recent years, many fault diagnosis methods have emerged in the field of fault diagnosis, such as the sparse decomposition method and empirical mode decomposition (EMD) method, which are widely used in the analysis of mechanical vibration signals. [3][4][5][6][7][8][9][10][11] Recently, a new method was proposed by YF Peng and JS Chen, which is called the adaptive sparsest narrow-band decomposition (ASNBD) method.…”

Section: Introductionmentioning

confidence: 99%

Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

Cheng

Chen

Luo

et al. 2020

Advances in Mechanical Engineering

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Adaptive sparsest narrow-band decomposition is the most sparse solution to search for signals in the over-complete dictionary library containing intrinsic mode functions, which transform the signal decomposition into an optimization problem, but the calculation accuracy must be improved in the case of strong noise interference. Therefore, in combination with the algorithm of the complementary ensemble empirical mode decomposition, a new method of the complementary ensemble adaptive sparsest narrow-band decomposition is obtained. In the complementary ensemble adaptive sparsest narrow-band decomposition, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters. The analysis results of the simulation and experimental data show this method is superior to complementary ensemble empirical mode decomposition and adaptive sparsest narrow-band decomposition in inhibiting the mode confusion, endpoint effect, improving the component orthogonality and accuracy, and effectively identifying the gears fault types.

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“…Gong et al [23] proposed an optimized multiscale morphology method based on conventional multiscale morphology and iterative morphology to effectively suppress noise and extract the impulsive features found in the vibration signals of faulty rolling element bearings. Li et al [24] proposed a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis for early fault diagnosis of rolling bearings. Deng et al [25] proposed a novel method called adaptive multiscale AVG-Hat morphology filter to detect and extract the fault features hidden in the heavy noise of the vibration signal.…”

Section: Introductionmentioning

confidence: 99%

Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy

Zhao

Yao

et al. 2018

Entropy

101

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A damage degree identification method based on high-order difference mathematical morphology gradient spectrum entropy (HMGSEDI) is proposed in this paper to solve the problem that fault signal of rolling bearings are weak and difficult to be quantitatively measured. In the HMGSEDI method, on the basis of mathematical morphology gradient spectrum and spectrum entropy, the changing scale influence of structure elements to damage degree identification is thoroughly analyzed to determine its optimal scale range. The high-order difference mathematical morphology gradient spectrum entropy is then defined in order to quantitatively describe the fault damage degree of bearing. The discrimination concept of fault damage degree is defined to quantitatively describe the difference between the high-order differential mathematical entropy and the general mathematical morphology entropy in order to propose a fault damage degree identification method. The vibration signal of motors under no-load and load states are used to testify the effectiveness of the proposed HMGSEDI method. The experiment shows that high-order differential mathematical morphology entropy can more effectively identify the fault damage degree of bearings and the identification accuracy of fault damage degree can be greatly improved. Therefore, the HMGSEDI method is an effective quantitative fault damage degree identification method, and provides a new way to identify fault damage degree and fault prediction of rotating machinery.

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Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings

Cited by 199 publications

References 43 publications

Adaptive Morphological Reconstruction for Seeded Image Segmentation

Adaptive Morphological Reconstruction for Seeded Image Segmentation

Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy

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