Classifying wear characteristics in lubricated sliding wear based on time series sensor signals using artificial intelligence

使用人工智能根据时间序列传感器信号对润滑滑动磨损的磨损特征进行分类

• 批准号: 525173005
• 负责人: Dr.-Ing. Stefanie Hanke
• 金额: --
• 依托单位: Lehrstuhl für Werkstofftechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/525173005?language=en
• 关键词: Classifying; wear; characteristics; lubricated; sliding

Friction and wear cause high economical losses in terms of energy and resources still today. Estimated numbers for losses due to friction and wear range at ≈23% of global energy consumption. While friction is assumed to cause significantly higher direct costs than wear, the adverse effects of wear are more difficult to describe, including amongst others aspects of environmental pollution or equipment downtime.Friction occurring in sliding surfaces usually reduces the efficiency of the technical system. Frictional energy is partially converted into thermal energy and enables the formation of wear particles. In addition, there are interactions with the dynamic behavior (stiffness, inertia) of the technical system in which the friction occurs. While a strong influence of material reactions - including wear - on the friction coefficient is clearly observed, no fundamental and general correlation between friction signal and wear mechanisms has been found, due to the high number of influencing parameters acting in different tribosystems. Still, in order to apply measures to mitigate wear, it is important to understand the wear mechanisms. Using easy to measure quantities such as friction force, temperature or vibrations of a machine to identify wear mechanisms taking place in a tribosystem - in the future also operando - is highly desirable.Novel, data-driven methods are a promising opportunity to address the intrinsic complexity of tribological problems. These include artificial intelligence (AI) and machine learning methods.In this project it will be investigated how reliably an AI can classify characteristic combinations of acting wear mechanisms and the resulting wear volumes, based on normal and friction forces, temperature evolution or high-frequency vibrations occurring in two different tribometers.To achieve these aims, high numbers of tribological tests are carried out in a controlled manner, yielding up to 5 classes of wear characteristics and wear volumes to train the AI. Their use on unseen data will show the potential of AI to classify wear characteristics and in the future possibly single wear mechanisms observed in sliding wear of metals on a fundamental level.

摩擦和磨损在当今的能源和资源方面造成了高度的经济损失，而摩擦的直接成本明显高于磨损，而磨损的不良影响则更多地描述了环境污染或设备停机时间。遇到n个滑动表面，摩擦能量会逐渐转换为摩擦粒子的形成，并使摩擦系统的动态行为（刚度，惯性）形成。出现材料反应的效果 - 包括摩擦系数，没有发现基本和一般的摩擦信号，并且磨损机制可以减轻磨损，这对于了解磨损力，温度或振动很重要。机器磨损机制发生在tribosystem中 - 未来的Alsondo中，数据驱动的方法是解决摩擦学的内在复杂性的有前途的机会。 。受控的耳朵特征和磨损量以训练AI的使用。