Bearing fault diagnosis based on an improved morphological filter

Hu, Zhiyong; Wang, Chao; Zhu, Jun; Xingchen, Liu; Kong, Fanrang

doi:10.1016/j.measurement.2015.11.028

Cited by 48 publications

(26 citation statements)

References 22 publications

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“…where ⊕ denotes the dilation operator, Θ denotes the erosion operator, ∘ stands for the opening operator, and • stands for the closing operator. Besides, several main morphological filters [13] are introduced as follows: Morphological gradient (MG):…”

Section: Basic Theory Of Mathematical Morphologymentioning

confidence: 99%

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Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter

Luo

Chen

Kang

et al. 2019

Shock and Vibration

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The extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing. However, the detection of weak fault signals generally suffers the strong background noise. To solve this problem, a new adaptive multiscale enhanced combination gradient morphological filter (MECGMF) is proposed for the fault diagnosis of rolling element bearing. In this method, according to the filtering ability of four basic morphological filter operators, an enhanced combination gradient morphological operation (ECGMF) is first proposed. This design enhances the ability of MECGMF to extract impulse signals from strong background noise. And accordingly, a new adaptive selection strategy named kurtosis fault feature ratio (KFFR) is proposed to select an optimal structuring element (SE) scale. Subsequently, the optimal SE scale is the largest measure of multiscale morphological filtering for extracting bearing fault information. In the meanwhile, the effectiveness of the proposed method is verified by simulation and experiment. Finally, the experimental results demonstrate that MECGMF can effectively restrain the noise interference and extract fault characteristic signals of rolling element bearing from strong background noise. Moreover, comparative tests show that the proposed method is more effective in detecting wind turbine bearing failures.

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Section: Basic Theory Of Mathematical Morphologymentioning

confidence: 99%

“…Based on the aforementioned four basic operators, some advanced cascading and combining operations can be implemented. For example, Hu et al [13] applied the morphological gradient (MG) operator to bearing fault diagnosis successfully. Also, Li et al [14] used MG to propose weight multiscale morphological filtering.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter

Luo

Chen

Kang

et al. 2019

Shock and Vibration

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“…Lv et al [ 13 ] introduced average a combination difference morphological filter for feature extraction, where the scale selection of structural elements (SEs) is determined by the Teager energy kurtosis (TEK). Hu et al [ 14 ] proposed a new improved MF algorithm to overcome the deficiency of conventional MFs which can be easily interfered with. In [ 15 ], a combination of a MF and translation-invariant wavelet decomposition was employed to augment ensemble empirical mode decomposition (EEMD) and to improve de-noising reliability.…”

Section: Introductionmentioning

confidence: 99%

Improved Morphological Filter Based on Variational Mode Decomposition for MEMS Gyroscope De-Noising

Shen

Cao

et al. 2018

Micromachines

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An adaptive multi-scale method based on the combination generalized morphological filter (CGMF) is presented for de-noising of the output signal from a MEMS gyroscope. A variational mode decomposition is employed to decompose the original signal into multi-scale modes. After choosing a length selection for the structure element (SE), the adaptive multi-scale CGMF method reduces the noise corresponding to the different modes, after which a reconstruction of the de-noised signal is obtained. From an analysis of the effect of de-noising, the main advantages of the present method are that it: (i) effectively overcomes deficiencies arising from data deviation compared with conventional morphological filters (MFs); (ii) effectively targets the different components of noise and provides efficacy in de-noising, not only primarily eliminating noise but also smoothing the waveform; and (iii) solves the problem of SE-length selection for a MF and produces feasible formulae of indicators such as the power spectral entropy and root mean square error for mode evaluations. Compared with the other current signal processing methods, the method proposed owns a simpler construction with a reasonable complexity, and it can offer better noise suppression effect. Experiments demonstrate the applicability and feasibility of the de-noising algorithm.

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“…They paid much attention to the relationship between the SE length and the filtering effect, and also suggested that the length was around 0.6 times the pulse repetition period. Hu et al [13] used the morphological gradient (MG) operator to pick up both positive and negative impulses from vibration signals successfully. Dong et al [14] put forward the average operator (AVG) for the impulse components extraction, and the length of SE was determined by an indicator called signal-to-noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

Fault feature extraction for rolling element bearings based on multi-scale morphological filter and frequency-weighted energy operator

Zhu¹,

Zhang²,

Zhu³

2018

J. vibroeng.

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In order to extract impulse components from bearing vibration signals with strong background noise, a fault feature extraction method based on multi-scale average combination difference morphological filter and Frequency-Weighted Energy Operator is proposed in this paper. The average combination difference morphological filter (ACDIF) is used to enhance the positive and negative impulse components in the signal. The double-dot structure element (SE) is used instead of zero amplitude flat SE to improve the effectiveness of fault feature extraction. The weight coefficients of the filtered results at different scales in multi-scale ACDIF are adaptively determined by an optimization algorithm called hybrid particle swarm optimizer with sine cosine acceleration coefficients (H-PSO-SCAC). At last, as the Frequency-Weighted Energy Operator (FWEO) outperforms the enveloping method in detecting impulse components of signals, the filtered signal is processed by FWEO to extract the fault features of bearings. Results on simulation and experimental bearing vibration signals show that the proposed method can effectively suppress noise and extract the fault features from bearing vibration signals.

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Bearing fault diagnosis based on an improved morphological filter

Cited by 48 publications

References 22 publications

Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter

Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter

Improved Morphological Filter Based on Variational Mode Decomposition for MEMS Gyroscope De-Noising

Fault feature extraction for rolling element bearings based on multi-scale morphological filter and frequency-weighted energy operator

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