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- W4300716570 abstract "Worm gearbox has wide range of applications such as in agitators, crushers, mixers, elevators, extruders, and cranes. The fault in gearbox leads to breakdown of the machinery. Early detection of fault in worm gearbox can prompt for economical preventive maintenance which ultimately prevents the breakdown and the production losses. Classification of the fault of worm gear is the first step of ensuring the gearbox protection. In this paper, a robust classification scheme based on autoregression minimum entropy deconvolution (AR-MED) and dendrogram support vector machine (DSVM) has been proposed to classify the faults of worm gearbox. AR-MED filter is used to remove the regular pattern of the gear and enhance the periodic impulsiveness in the signal. Features have been extracted from the filtered signal and used as input in DSVM model for multiclass classification of the fault conditions of worm gearbox. Results reveal that effectiveness of classification of DSVM has been enhanced using AR-MED filter with 100% decoding accuracy." @default.
- W4300716570 created "2022-10-04" @default.
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- W4300716570 date "2022-10-04" @default.
- W4300716570 modified "2023-10-18" @default.
- W4300716570 title "Classification of Worm Gearbox Fault Using Dendrogram Support Vector Machine" @default.
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- W4300716570 doi "https://doi.org/10.1007/978-981-19-3716-3_21" @default.
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