Evaluation of performance of vibration signatures for condition monitoring of worm gearbox by using ANN

Barshikar, Raghavendra R.; Baviskar, Prasad Ramchandra

doi:10.1007/s12008-023-01268-x

Cited by 1 publication

(4 citation statements)

References 24 publications

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“…four variables, as illustrated in Table 1. In the present study, artificial defects were created on worm gear and bearing inner and outer races, as in [24]. The example of an artificially created fault is shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

“…Worm gear gear mesh frequency, outer race worm gear bearing pass frequency, and inner race worm gear bearing pass frequency, as determined in [24], are all equal to 96 Hz, 15.85 Hz, and 22.54 Hz, respectively. The experiments were performed for intact and twenty-seven fault conditions in a gearbox.…”

Section: Laboratory Experimental Test Rigmentioning

confidence: 99%

“…Accelerometer position.The OR 34 FFT analyzer is compatible with the NVGate V10.00 software[24]. Based on the available literature, for slow rotational speeds and characteristic rotational frequencies less than 100 Hz, the wavelet denoise method based on the biorthogonal sigmoid thresholding algorithm[14,24] is implemented to reconstruct contaminated vibration signatures, which may include other components' vibration and noise. NVGate V10.00 software is utilized to perform wavelet denoising.…”

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confidence: 99%

“…Training, testing, and validation are the three categories into which the ANN model is divided. Out of the total data, 70% was used for training while testing and validation, each was done using 15% of the data[22,[24][25]. After training ANN model; their performance are check.Performance of trained ANN model is based on mean square error (MSE) and regression R value.…”

mentioning

confidence: 99%

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Defect Categorization of Ribbon Blender Worm Gearbox Worm Wheel and Bearing Based on Artificial Neural Network

Barshikar,

Ghongade,

Bhadre

et al. 2024

Eksploatacja i Niezawodność – Maintenance and Reliability

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There is a demand for worm gearboxes in diversified industrial fields that include machinery such as escalators, ribbon blenders, pulverisers, bowl mills, etc. because of their peculiar characteristics like torque and quick retardation. The most commonly occurring defects in a worm gear box are scratches that develop in the worm gear and in bearings. Early defect categorization is required to prevent a sudden breakdown that would decrease production. The defect is depicted in different cases, which include defects in the gear tooth and the outer and inner races of the bearing. In another case, the defect is considered in the gear tooth as well as the bearing. The severity is designated using the ANN. The experiments were performed under these conditions with a good worm gearbox to capture vibration response signatures. Using these values as an input to the ANN, the model is trained. Experimental results show that vibration amplitude increases with fault progression in the worm gearbox, and the trained ANN model effectively categorizes worm gearbox faults with an accuracy of 97.12%.

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Section: Methodsmentioning

confidence: 99%

Section: Laboratory Experimental Test Rigmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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