Automated gear fault detection of micron level wear in bevel gears using variational mode decomposition

Ramteke, Dada Saheb; Parey, Anand; Pachori, Ram Bilas

doi:10.1007/s12206-019-1123-2

Cited by 20 publications

(5 citation statements)

References 30 publications

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“…Li et al [5] adopted a combination of VMD and a deep neural network to reduce the original signal features and classification of gear faults. Ramteke et al [6] used VMD to diagnose the wear gear faults. Li et al [7] proposed a gearbox impact fault detection method by combining VMD and coupled underdamped stochastic resonance, which can be used to extract the periodic impact characteristics.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of tooth surface spalling based on variational mode decomposition and maximum correlation kurtosis method

Liu,

Cheng,

Xiong

2024

Eng. Res. Express

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In view of the phenomenon that the early fault features of tooth surface spalling are very weak and difficult to be extracted due to random noise and other signal interference, a method combining maximum correlation kurtosis uncoiling and variational mode decomposition was proposed to extract the fault features of tooth surface spalling. Firstly, a series of modes are obtained by variational mode decomposition, and the kurtosis criterion is applied to select the modes with rich fault information for reconstruction and noise reduction. Secondly, the maximum correlation kurtosis deconvolution method is used to enhance the selected signals. Finally, the fault features are extracted by envelope demodulation of the reconstructed signal. The effectiveness of this method is verified by analysis, and the different frequency components of the vibration signal of tooth surface spalling fault can be accurately separated.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis of tooth surface spalling based on variational mode decomposition and maximum correlation kurtosis method

Liu,

Cheng,

Xiong

2024

Eng. Res. Express

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“…Upadhyay et al [14] presented a new technique based on tunable Q-wavelet transform and fractal-based features for the diagnosis of bearing defects. WT struggles with the dilemma of which mother wavelet to use and how many decomposition levels to use [23]. The WVD, on the other hand, offers a better time-frequency resolution; however, it does contain a few cross-terms [23].…”

Section: Introductionmentioning

confidence: 99%

“…WT struggles with the dilemma of which mother wavelet to use and how many decomposition levels to use [23]. The WVD, on the other hand, offers a better time-frequency resolution; however, it does contain a few cross-terms [23]. For the investigation of nonstationary signals, Gilles [24] developed an innovative constructing approach called the empirical wavelet transform (EWT).…”

Section: Introductionmentioning

confidence: 99%

A New Automated Classification Framework for Gear Fault Diagnosis Using Fourier–Bessel Domain-Based Empirical Wavelet Transform

Ramteke,

Parey,

Pachori

2023

Machines

Self Cite

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Gears are the most important parts of a rotary system, and they are used for mechanical power transmission. The health monitoring of such a system is needed to observe its effective and reliable working. An approach that is based on vibration is typically utilized while carrying out fault diagnostics on a gearbox. Using the Fourier–Bessel series expansion (FBSE) as the basis for an empirical wavelet transform (EWT), a novel automated technique has been proposed in this paper, with a combination of these two approaches, i.e., FBSE-EWT. To improve the frequency resolution, the current empirical wavelet transform will be reformed utilizing the FBSE technique. The proposed novel method includes the decomposition of different levels of gear crack vibration signals into narrow-band components (NBCs) or sub-bands. The Kruskal–Wallis test is utilized to choose the features that are statistically significant in order to separate them from the sub-bands. Three classifiers are used for fault classification, i.e., random forest, J48 decision tree classifiers, and multilayer perceptron function classifier. A comparative study has been performed between the existing EWT and the proposed novel methodology. It has been observed that the FBSE-EWT with a random forest classifier shows a better gear fault detection performance compared to the existing EWT.

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“…At present, the researches of gear transmission mainly focus on gear life assessment techniques [3][4][5][6], theoretical research on gear reinforcement [7][8][9][10], computer-aided design method of gear [11][12][13] and gear fault detection research [14][15][16][17][18]. There are few theoretical studies on gear mathematical models based on numerical analysis methods.…”

Section: Introductionmentioning

confidence: 99%

Transmission Design of Bevel Gear with Arbitrary Crossed Axes and Its Application in the Knotter

Yin

Han

Zheng

et al. 2021

Preprint

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The bevel gear transmission with crossed axes is widely used, but there are the difficulty of tooth profile calculation and modeling in the design and manufacturing process. This paper analyzes firstly the parameter solving equation of the bevel gear pair with arbitrary crossed axes. Based on the meshing principle of the bevel gear pair and numerical analysis method, the meshing equations of the bevel gear pair with crossed axes are derived, which provide the calculation models for the design of spatial bevel gear tooth surface. Taking the bevel gear pair with crossed axes in two kinds of the knotter as two design examples, the geometric parameters of the bevel gear pair in the knotter are solved, and the mathematical models of the tooth surface are programmed and calculated by using MATLAB. Through the graphic display of the tooth surface point set under MATLAB software and the 3D modeling function of Pro/Engineering software, the accurate 3D models of the bevel gear tooth surface are established. The meshing transmission simulations of the established bevel gear pair are respectively carried out by using ADAMS, and their physical prototype and transmission tests are also implemented. The test results showed that the transmission of the designed bevel gear pair is accurate and stable, which proves the correctness of the derived calculation model of the tooth surface of the bevel gear with crossed axes.

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Automated gear fault detection of micron level wear in bevel gears using variational mode decomposition

Cited by 20 publications

References 30 publications

Fault diagnosis of tooth surface spalling based on variational mode decomposition and maximum correlation kurtosis method

Fault diagnosis of tooth surface spalling based on variational mode decomposition and maximum correlation kurtosis method

A New Automated Classification Framework for Gear Fault Diagnosis Using Fourier–Bessel Domain-Based Empirical Wavelet Transform

Transmission Design of Bevel Gear with Arbitrary Crossed Axes and Its Application in the Knotter

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