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- W2980371010 abstract "The gearbox is an important component widely used in mechanical system. Many researches have been conducted for effective fault diagnosis of gearbox to avoid unexpected accidents. It is a great challenge to extract representative features from the raw gearbox signals submerged by noise. Morphological filtering is proved to be effective to denoise the mechanical fault signals. However, it is difficult to determine the suitable length of the structure element (SE). In addition, intelligent classification algorithms are propitious to reduce artificial misjudgment caused by lack of professional knowledge. Thus, this article presents a fault diagnosis method combing adaptive morphological filtering and support vector regressive (SVR). The adaptive morphological filter is firstly applied to denoise the signals, which can determine the length of SE adaptively. Then, nine statistical features with sensitivity and stability are extracted from the denoised signals. Finally, SVR is applied for intelligent fault recognition. Gearbox datasets are analyzed to validate the effectiveness of the proposed method and the results fully demonstrate its superiority in noise removal and fault diagnosis compared to other diagnosis methods." @default.
- W2980371010 created "2019-10-25" @default.
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- W2980371010 date "2019-07-01" @default.
- W2980371010 modified "2023-09-25" @default.
- W2980371010 title "Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Gearbox Fault Diagnosis" @default.
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- W2980371010 doi "https://doi.org/10.1109/aim.2019.8868358" @default.
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