This paper introduces a novel parameter related to bearing degradation, namely the Equivalent Damaged Volume (EDV). An algorithm capable of extracting EDV values from experimental data is detailed. To this end, the proposed technique relies on the comparison between experimental and numerical signals. The former are the result of an extensive campaign of run-to-failure tests performed on a dedicated test rig. On the other hand, the latter are obtained by means of a lumped parameter model that accounts for the angular extent and the depth of either localized or extended defects. The potential of this novel technique is demonstrated by exploiting the values obtained by the algorithm as inputs for a prognostic model. In particular, the developed model aims at predicting the time until a certain threshold on the equivalent damaged volume is crossed, regardless of the applied load and the shaft rotation speed. The advantages and downsides introduced by the proposed approach are thoroughly discussed by providing examples based on real degradation histories.

中文翻译：

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基于物理的滚动轴承预测：等效损坏体积算法

本文介绍了一个与轴承退化相关的新参数，即等效损坏体积 (EDV)。详细介绍了一种能够从实验数据中提取 EDV 值的算法。为此，所提出的技术依赖于实验信号和数值信号之间的比较。前者是在专用试验台上进行的大量运行至故障测试的结果。另一方面，后者是通过集中参数模型获得的，该模型考虑了局部或扩展缺陷的角度范围和深度。通过利用算法获得的值作为预后模型的输入，证明了这种新技术的潜力。特别是，所开发的模型旨在预测超过等效损坏体积的某个阈值之前的时间，无论施加的载荷和轴转速如何。通过提供基于真实退化历史的示例，彻底讨论了所提出方法的优点和缺点。