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.

如今，摩擦和磨损在能源和资源方面造成了很高的经济损失。由于摩擦和磨损范围造成的损失数量的估计数，占全球能源消耗的约23％。虽然假定摩擦会导致直接成本明显高于磨损，但磨损的不利影响更难描述，包括环境污染或设备停机时间的其他方面。在滑动表面中发生陷阱通常会降低技术系统的效率。摩擦能量部分转化为热能，并实现磨损颗粒的形成。此外，与发生摩擦发生的技术系统的动态行为（刚度，惯性）存在相互作用。虽然清楚地观察到材料反应（包括磨损）对摩擦系数的强大影响，但由于在不同的Tribosystems中作用的影响参数的数量大量，因此未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，为了进行测量以减轻磨损，了解磨损机制很重要。使用易于测量的数量，例如摩擦力，温度或机器的振动来识别在摩擦系统中发生的磨损机制（将来也是Operando）是非常可取的。NOVEL，数据驱动的方法是解决培养基问题的内在复杂性的有望机会。这些包括人工智能（AI）和机器学习方法。在该项目中，将研究AI如何根据正常和摩擦力，温度进化或高频率振动来对表演磨损机制的特征组合和由此产生的磨损量进行分类。训练AI的量。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，并且将来可能在基本水平的金属滑动磨损中观察到的单磨损机制。