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.

摩擦和磨损造成的能源和资源方面的高经济损失至今仍然存在。由于摩擦和磨损造成的损失估计占全球能源消耗的23%。虽然摩擦造成的直接成本比磨损高得多，但磨损的负面影响更难描述，包括环境污染或设备停机等方面。发生在滑动表面上的摩擦通常会降低技术系统的效率。摩擦能部分转化为热能，并形成磨损颗粒。此外，存在与发生摩擦的技术系统的动态行为（刚度、惯性）的相互作用。虽然材料反应（包括磨损）对摩擦系数的影响很大，但由于不同摩擦系统中的影响参数很多，因此没有发现摩擦信号和磨损机制之间的基本和一般相关性。尽管如此，为了采取措施减轻磨损，重要的是要了解磨损机制。使用易于测量的量，如摩擦力，温度或振动的机器，以确定磨损机制发生在摩擦系统-在未来也operando -是非常可取的。新的，数据驱动的方法是一个有前途的机会，以解决摩擦学问题的内在复杂性。这些方法包括人工智能（AI）和机器学习方法。在该项目中，将研究AI如何可靠地根据两个不同摩擦计中发生的法向力和摩擦力、温度变化或高频振动，对作用磨损机制和由此产生的磨损量的特征组合进行分类。为了实现这些目标，以受控方式进行大量摩擦学测试，产生多达5类磨损特征和磨损量来训练AI。它们在看不见的数据上的使用将显示AI对磨损特性进行分类的潜力，并且在未来可能在基本水平上观察到金属滑动磨损中的单一磨损机制。