支齿轮损坏建模的数学复杂性

Machines Pub Date : 2024-05-16 DOI:10.3390/machines12050346
Aselimhe Oreavbiere, Muhammad Khan
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摘要

分析建模是获得齿轮动态响应或振动模式的有效方法,可用于健康监测和使用寿命预测。许多研究人员已经利用齿轮中常见的各种故障条件对这种响应进行了建模。这些模型的结果很好地说明了齿轮在不同健康状态下的动态响应变化。因此,目前已经有了一个响应目录,这应该有助于通过振动模式预测实际齿轮的健康状况。然而,这些分析模型在提供有用寿命预测解决方案方面存在局限性。这可能是因为这些模型大多使用单一故障条件进行建模,而不能有效地预测经历多个故障条件的齿轮的剩余寿命。与齿轮故障和动态建模相关的现有综述可提供有关故障模式、建模方法和健康预测的概述。然而,这些综述无法提供单一故障动态模型的关键异同点,从而无法确定在组合故障模式下开发模型的可能性。本文回顾了现有的正齿轮分析模型,以及它们在预测齿轮健康状态方面所面临的相关挑战。特别是在模拟组合故障及其对齿轮动态响应和健康预测可能产生的影响方面,提出了建立更切合实际的模型的建议。
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Mathematical Complexities in Modelling Damage in Spur Gears
Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a good idea about the changes in the dynamic response available between different gear health states. Hence, a catalogue of the responses is currently available, which ought to aid predictions of the health of actual gears by their vibration patterns. However, these analytical models are limited in providing solutions to useful life prediction. This may be because a majority of these models used single fault conditions for modelling and are not valid to predict the remaining life of gears undergoing more than one fault condition. Existing reviews related to gear faults and dynamic modelling can provide an overview of fault modes, methods for modelling and health prediction. However, these reviews are unable to provide the critical similarities and differences in the single-fault dynamic models to ascertain the possibility of developing models under combined fault modes. In this paper, existing analytical models of spur gears are reviewed with their associated challenges to predict the gear health state. Recommendations for establishing more realistic models are made especially in the context of modelling combined faults and their possible impact on gear dynamic response and health prediction.
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