Autarkic sensory foil bearing

空气轴承用于超高速轴(&gt；100,000转/分)，因为它具有低的润滑间隙摩擦力和污染系列润滑。这使得它们在空气压缩机和医疗应用中特别有趣。尽管空气轴承在运行中几乎没有摩擦，但低速范围的特点是高水平的混合摩擦。由于空气的低速和低粘度，不能形成足够承载的润滑膜。在此范围内频繁骑行或长时间停留会导致磨损显著增加，从而缩短剩余使用寿命。过载、严重不平衡或外倾也会损坏空气轴承。制造偏差加剧了这种影响，其影响很难预测。该项目的目标是通过传感器集成实现对薄膜空气轴承的在线状态监测。在线监测可以清楚地识别不利的工作点，并更准确地估计剩余寿命，从而有助于提高可靠性和及早发现故障。相关的轴承参数包括最小润滑间隙高度、润滑间隙温度或薄膜轴承中当前的油膜形状和油膜温度、轴中心点的轨迹曲线或轨迹以及轴承中的磨损。因此，挑战在于集成合适的传感器来记录气动轴承中的相关测量变量。这意味着对这些传感器的集成性和可靠性有很高的要求，这些传感器还必须在高速或高频激励范围内可靠地工作，并可能在高轴承温度下工作。这种状态监测是通过完全集成复杂的状态监测系统来实现的。在轴承中进行供电、数据采集、准备、处理和评估。处理后，通过无线数据传输转发承载状态。为了评估运行状况，监测了轴承的温度、气隙、不平衡、速度、温度、油膜伸长和轴倾斜。评估是通过在轴承内的微控制器中实现的人工神经网络来进行的。传感器集成轴承的工作点、能量产生、数据采集和处理正在研究中，并在两个试验台的初始原型的帮助下进一步开发。正在对高达30,000转/分的启动和升空行为以及高达120,000转/分的运行行为进行监测。这里获得的数据将用于进一步细化网络和改进原型。

Air bearings are used for very high speed shafts (>100,000 rpm) due to the low lubrication gap friction and the pollutant series lubrication. This makes them particularly interesting for air compressors and in medical applications. Even though air bearings are almost frictionless in operation, the lower speed range is characterized by high level of mixed friction. Due to the low speed and the low viscosity of the air, a sufficiently load-bearing lubricant film cannot be formed. Frequent cycling or long dwell times in this range lead to significantly increased wear and thus to a reduction in the remaining service life. Overload, severe imbalance or camber can also damage the air bearing. This effect is intensified by manufacturing deviations, the effects of which are difficult to predict. The aim of the project is to achieve on-line condition monitoring of film air bearings by sensor integration. On-line monitoring allows a clean identification of unfavorable operating points and a more accurate remaining life estimation, thus contributing to greater reliability and early detection of failures. Relevant bearing parameters are the minimum lubrication gap height, the lubrication gap temperature, or current film shape and film temperature in film bearings, the trajectory curve or orbit of the shaft center point and the wear in the bearing. The challenge is therefore to integrate suitable sensors for recording the relevant measured variables in the aerodynamic bearing. This implies high demands on the integrability and reliability of these sensors, which must also function reliably in the range of high speeds or with high-frequency excitation and possibly at high bearing temperatures.This condition monitoring is achieved by the complete integration of a complex condition monitoring system. Power supply, data acquisition, preparation, processing and evaluation are performed in the bearing. After processing, the bearing status is forwarded by wireless data transmission. To evaluate the operating condition, the temperature, air gap, unbalance, speed, temperature of the bearing, film elongation and shaft tilting are monitored. The evaluation is carried out by an artificial neural network which is implemented in a microcontroller located in the bearing. The operating points, energy generation, data acquisition and processing of the sensor-integrated bearing are being investigated and further developed with the aid of an initial prototype in two test rigs. The monitoring of the start-up and lift-off behavior up to 30,000 rpm and the operating behavior up to 120,000 rpm are being tested. The data obtained here will be used to further refine the network and improve the prototype.