A Low-Rank and Sparse Decomposition-Based Method of Improving the Accuracy of Sub-Pixel Grayscale Centroid Extraction for Spot Images

Dong, Ziqiang; Sun, Xingwei; Xu, Fangsu; Liu, Weijun

doi:10.1109/jsen.2020.2974725

Cited by 18 publications

(10 citation statements)

References 28 publications

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“…Therefore, the EAVGH-CSC-EES method proposed in this paper is used to process the signal y(t). In EAVGH-CSC-EES method, the window length Nw is 2 6 , cyclic frequency resolution is 0.1Hz, the maximum cycle frequency αmax is 100Hz, w[n] is selected as the Hanning window, the result of the processing is shown in Fig. 10.…”

Section: T Y T Y T Y T Y T T Y T a S T It Y T B S T Ntmentioning

confidence: 99%

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Rolling Bearing Fault Diagnosis Method With Enhanced Top-Hat Transform Filtering and Cyclic Spectrum Coherence

et al. 2020

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As an important component of rotating machinery, the fault information of rolling element bearing is difficult to be recognized due to the background noise and harmonic frequency contained in the tested vibration signal. In order to accurately and completely extract the fault characteristic information from the vibration signal, a fault diagnosis research method (EAVGH-CSC-EES) based on the combination of enhanced top-hat morphological filtering (EAVGH) and cyclic spectrum coherence (CSC) is proposed. First of all, in view of the problem that the existing top-hat operators cannot fully extract the signal fault characteristics, this paper selects the optimal operator from the four enhanced morphology operators to construct the EAVGH. Since the reasonable selection of structural element (SE) scale has a great influence on the filtering result of morphological operators, then this paper applies feature energy factor (FEF) to select the optimal scale of SE. Subsequently, in order to further solve the influence of the non-linear modulation frequency components in the signal, while improving the filtering performance of EAVGH. This paper uses the cyclic spectrum coherence function (CSC) to further process the filtered signal. And then the enhanced envelope spectrum (EES) of the signal is obtained. Simulation and two sets of bearing fault experiments verify the rationality and effectiveness of the EAVGH-CSC method. The comparison results with other existing methods can further prove the superiority of the method proposed in this paper. INDEX TERMS The bearing fault diagnosis, morphological filtering, enhanced top-hat morphological operator, cyclic spectrum coherence.

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Section: T Y T Y T Y T Y T T Y T a S T It Y T B S T Ntmentioning

confidence: 99%

“…This brings great difficulties to the fault diagnosis of the equipment. Therefore, how to effectively and accurately identify the fault impact signal of the rolling bearing from the vibration signal with strong background noise has become a key research issue [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Method With Enhanced Top-Hat Transform Filtering and Cyclic Spectrum Coherence

et al. 2020

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“…In most cases, they are undesirable and harmful, as they shorten the service life of equipment and even cause serious accidents [1]. Recently, with the rapid development of sophisticated industry, such as high-speed trains [2], precision optical instruments [3] and stealth submarines [4], the requirements on the vibration environment have become increasingly strict. Considering the contradiction between the load-bearing capacity and the low-frequency vibration isolation of traditional linear vibration isolators, nonlinear theory is widely adopted for better isolation performance [5].…”

Section: Introductionmentioning

confidence: 99%

A Vibration Isolation System Using the Negative Stiffness Corrector Formed by Cam-Roller Mechanisms with Quadratic Polynomial Trajectory

et al. 2020

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The vibration isolator equipped with a negative stiffness corrector (NSC) excels at vibration isolation, but its stiffness often presents complex nonlinearity which needs to be approximated in calculation. To avoid the harmful effects of approximate stiffness, the NSC formed by the cam-roller mechanism with a quadratic polynomial trajectory (QCRM) is proposed to construct the vibration isolation system. From the inherent geometrical relationship in the structure, the generation mechanism of high-static-low-dynamic stiffness is analyzed, and the quasi-zero stiffness (QZS) condition of the system is derived. Based on the dynamic model of the QZS vibration isolator, the functions of response characteristics are solved by the harmonic balance method. Then, the absolute displacement transmissibility with different parameter values, and the vibration isolation performance under sinusoidal, multi-frequency wave, and random excitations are discussed. The simulated results show that the stiffness expression of the proposed QZS vibration isolator is directly a quadratic function, which removes the calculation error caused by approximate stiffness at large displacement and broadens the available isolation displacement range. Introducing the QCRM-NSC can significantly suppress the low-frequency vibration and resonance response without changing the load-bearing capacity of the vibration isolator. Under various excitations, the vibration isolation performance of the QZS vibration isolator all outperforms the linear counterpart.

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“…However, the fault impulses in the vibration signal are always overwhelmed by a large amount of background noise and complex interferences. Therefore, an effective fault feature extraction method may be very important for fault diagnosis of rolling bearings [3,4].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Principal Component Analysis Assisted New Optimal Scale Morphological Top-Hat Filter for the Fault Diagnosis of Rolling Bearing

2020

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The early fault impulses of rolling bearing are often submerged by harmonic interferences and background noise. In this paper, a fault diagnosis scheme called probabilistic principal component analysis assisted optimal scale average of erosion and dilation hat filter (OSAEDH-PPCA) is presented for the fault detection of rolling bearing. Based on morphological erosion operator and morphological dilation operator, a new morphological top-hat operator, namely average of erosion and dilation hat (AEDH) operator is firstly proposed to extract the fault impulses in the vibration signal. Simulation analysis shows the filter characteristics of proposed AEDH operator. Comparative analyses demonstrate that the feature extraction property of the AEDH operator is superior to existing top-hat operators. Then, the probabilistic principal component analysis is introduced to enhance the filter property of AEDH for highlighting the fault feature information of rolling bearing further. Experimental signals collected from the test rig and the engineering are employed to validate the availability of proposed method. Experimental results show that the OSAEDH-PPCA can effectively extract the early fault impulses from vibration signal of rolling bearing. Comparison results verify that the OSAEDH-PPCA has advantage in early fault detection of rolling bearing than other morphological filters in existence. INDEX TERMSRolling bearing; Morphological filter; Morphological operator; Probabilistic principal component analysis; Fault diagnosis. NOMENCLATURE Acronyms MM mathematical morphology MF morphological filter AVG average of opening and closing CMF average of opening-closing and closing-opening MG gradient of dilation and erosion DIF gradient of opening and closing AVGH average of opening and closing hat CMFH average of opening-closing and closing-opening hat SE structure element WTH white top-hat AED average of erosion and dilation AEDH average of erosion and dilation hat OSAEDH optimal scale average of erosion and dilation hat filter PPCA probabilistic principal component analysis CC correlation coefficient FEF feature energy factor SNR signal-to-noise ratio AMCMFH adaptive multiscale CMFH transform AMAVGH adaptive multiscale AVGH transform AMMGDE averaged multiscale MG filter AMMGCO averaged multiscale DIF filter ACDIF average combination difference morphological filter

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A Low-Rank and Sparse Decomposition-Based Method of Improving the Accuracy of Sub-Pixel Grayscale Centroid Extraction for Spot Images

Cited by 18 publications

References 28 publications

Rolling Bearing Fault Diagnosis Method With Enhanced Top-Hat Transform Filtering and Cyclic Spectrum Coherence

Rolling Bearing Fault Diagnosis Method With Enhanced Top-Hat Transform Filtering and Cyclic Spectrum Coherence

A Vibration Isolation System Using the Negative Stiffness Corrector Formed by Cam-Roller Mechanisms with Quadratic Polynomial Trajectory

Probabilistic Principal Component Analysis Assisted New Optimal Scale Morphological Top-Hat Filter for the Fault Diagnosis of Rolling Bearing

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