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Gear pitting severity level identification using binary segmentation methodology
With growth of a defect on the gear tooth surface, vibration response of the geared rotor system changes, which can be quantified using a health indicator. Based on the health indicator, identification of an accurate health stage or categorization is crucial so that the pitting severity classification can be done precisely. In all the prior reported works, the fault severity classification approaches are applied to the seeded pitting fault, and hence, exact state change point is known in advance. However, in the real life, the gear tooth surface is subjected to a natural pitting progression, and exact state change point of the gear is not known a priori. This study presents a binary segmentation methodology for identification of multiple degradation stages such as initial pitting, medium pitting, and severe pitting in a spur gear subjected to natural pitting progression. The gear damage progression is represented by a correlation coefficient‐based degradation parameter extracted from the residual vibration signal. Based on the change in distributional properties such as mean and variance of the degradation parameter with time progression, three degradation stages of the gear are identified. A flexible miniature inspection camera is used to access and assess the actual physical damage on the gear tooth surface. Based on the visual inspection pictures, actual damage area at the identified degradation stages is quantified. Performance of the binary segmentation methodology is validated through six run‐to‐failure accelerated gear pitting experiments.
Gear pitting severity level identification using binary segmentation methodology
With growth of a defect on the gear tooth surface, vibration response of the geared rotor system changes, which can be quantified using a health indicator. Based on the health indicator, identification of an accurate health stage or categorization is crucial so that the pitting severity classification can be done precisely. In all the prior reported works, the fault severity classification approaches are applied to the seeded pitting fault, and hence, exact state change point is known in advance. However, in the real life, the gear tooth surface is subjected to a natural pitting progression, and exact state change point of the gear is not known a priori. This study presents a binary segmentation methodology for identification of multiple degradation stages such as initial pitting, medium pitting, and severe pitting in a spur gear subjected to natural pitting progression. The gear damage progression is represented by a correlation coefficient‐based degradation parameter extracted from the residual vibration signal. Based on the change in distributional properties such as mean and variance of the degradation parameter with time progression, three degradation stages of the gear are identified. A flexible miniature inspection camera is used to access and assess the actual physical damage on the gear tooth surface. Based on the visual inspection pictures, actual damage area at the identified degradation stages is quantified. Performance of the binary segmentation methodology is validated through six run‐to‐failure accelerated gear pitting experiments.
Gear pitting severity level identification using binary segmentation methodology
Kundu, Pradeep (author) / Darpe, Ashish K. (author) / Kulkarni, Makarand S. (author)
2020-03-01
20 pages
Article (Journal)
Electronic Resource
English
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