Identification of planetary gearbox weak compound fault based on parallel dual-parameter optimized resonance sparse decomposition and improved MOMEDA

Wang, Chaoge; Li, Hongkun; Ou, Jin Ping; Hu, Ruijie; Hu, Shaoliang; Liu, Aiqiang

doi:10.1016/j.measurement.2020.108079

Cited by 36 publications

(33 citation statements)

References 40 publications

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“…The parameters that affected this second type of analysis were related to the evaluation of the acoustic emissions, for which we focused on the friction, lubrication, and rotation speed. The second type of material was not included among the variables, as the required number of simulations involving analyses requiring long computation times would have greatly increased [ 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

Noise Reduction in Spur Gear Systems

Liguori

Armentani

Bertocco

et al. 2020

Entropy

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This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, material, and lubrication, in order to validate ideas from the literature and to make several comparisons. Furthermore, a coupled Eulerian–Lagrangian (CEL) analysis was performed, which was an innovative way of evaluating the sound pressure level of the aforementioned gears. Different parameters were considered again, such as the friction, lubrication, material, and rotational speed, in order to make different research comparisons. The analytical results agreed with those in the literature, both for the static analysis and CEL analysis—for example, it was shown that changing the material from steel to ductile iron improved the gear noise, while increasing the rotational speed or the friction increased the acoustic emissions. Regarding the CEL analysis, air was considered a perfect gas, but its viscosity or another state equation could have also been taken into account. Therefore, the above allowed us to state that research into these scientific fields will bring about reliable results.

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Section: Resultsmentioning

confidence: 99%

Noise Reduction in Spur Gear Systems

Liguori

Armentani

Bertocco

et al. 2020

Entropy

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“…According to [ 38 ] and [ 30 ], we analyze the outer race fault signal with the fault period T = (90,134) and filter length L = 500. The multi-point kurtosis diagram is shown in Figure 10 .…”

Section: Experimental and Comparative Analysismentioning

confidence: 99%

“…Hence, unreasonable filter length L will weaken or enhance the energy of the original signal, and then cause misdiagnosis. To address the above-mentioned issues, scholars have introduced some optimization algorithms [ 36 , 37 ] to determine the fault period T and filter length L of MOMEDA, such as particle swarm optimization (PSO) algorithm [ 27 , 38 ], grasshopper optimization algorithm (GOA) [ 39 ] and so on. These parameter optimization algorithms mostly take kurtosis, envelope spectrum kurtosis (ESK) and other similar indexes as objective functions to improve MOMEDA.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy

Wang

et al. 2021

Sensors

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As a vital component widely used in the industrial production field, rolling bearings work under complicated working conditions and are prone to failure, which will affect the normal operation of the whole mechanical system. Therefore, it is essential to conduct a health assessment of the rolling bearing. In recent years, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault feature extraction for rolling bearings. However, the algorithm still has the following problems: (1) The selection of fault period T depends on prior knowledge. (2) The accuracy of signal denoising is affected by filter length L. To solve the limitations, an improved MOMEDA (IMOMEDA) method is proposed in this paper. Firstly, the envelope harmonic-to-noise ratio (EHNR) spectrum is adopted to estimate the fault period of MOMEDA. Then, the improved grid search method with EHNR spectral entropy as the objective function is constructed to calculate the optimal filter length used in the MOMEDA. Finally, a feature extraction method based on the improved MOMEDA (IMOMEDA) and Teager-Kaiser energy operator (TKEO) is applied in the field of rolling bearing fault diagnosis. The effectiveness and generalization performance of the proposed method is verified through comparison experiment with three data sets.

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“…Currently, there are two common methods for pre-selecting the signal period. The first is to construct a suitable multi-objective optimization function and then use optimization algorithms such as grid search [ 18 ], grasshopper optimization algorithm [ 19 ], or particle swarm optimization [ 20 ] to identify the optimal period and filter length. However, these methods often require dozens of iterations, which is computationally complex and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

Kou

et al. 2021

Entropy

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Low-speed hoist bearings are characterized by fault features that are weak and difficult to extract. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is an effective method for extracting periodic pulses in a signal. However, the decomposition effect of MOMEDA largely depends on the selected pulse period and filter length. To address these drawbacks of MOMEDA and accurately extract features from the vibration signal of a hoist bearing, an adaptive feature extraction method is proposed based on iterative autocorrelation (IAC) and MOMEDA. To automatically identify the pulse period, a new evaluation index named autocorrelation kurtosis entropy (AKE) was constructed to select the optimal IAC. To eliminate the influence of the filter length on the decomposition effect, an iterative MOMEDA strategy was designed to gradually enhance signal impulse features. The Case Western Reserve University bearing dataset and bearing data from a self-made hoisting test setup were used to verify the effectiveness of IAC-MOMEDA in extracting weak features. Moreover, the capability of IAC-MOMEDA for features extraction of normal bearing vibration signal was further confirmed by field test data.

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Identification of planetary gearbox weak compound fault based on parallel dual-parameter optimized resonance sparse decomposition and improved MOMEDA

Cited by 36 publications

References 40 publications

Noise Reduction in Spur Gear Systems

Noise Reduction in Spur Gear Systems

An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

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