Remaining Useful Lifetime for New and Used Technical Systems under Non-Stationary Conditions

基于状态的维护和预测性维护由于其确保被监控系统的最佳利用的能力而越来越多地应用于工业中。这些维护策略允许诊断和预测系统在静止操作条件下的健康状态。然而，技术系统大多在非平稳条件下操作，例如，风力涡轮机由于随机风激励而受到不同负载和速度的影响。非平稳条件导致传感器数据改变，从而掩盖由系统故障或退化引起的改变。因此，状态监测方法需要扩展和适应非平稳条件下运行的系统。拟议项目的目的是开发非平稳条件下运行的系统的剩余使用寿命预测方法。因此，工程领域的经典数据驱动和基于模型的方法与人工智能领域的方法相结合。通过聚类和分类与基于知识的方法的混合组合，操作条件进行分类，并确定故障模式。基于不确定性量化和分析的操作条件之间的关系，传感器数据和退化演变，适当的功能，使剩余使用寿命的预测开发和评估。通过使用不同的机器学习方法（例如对数据流的学习）来实现嵌入非平稳的未来操作条件。这些方法能够实现增量学习和适应变化，如操作条件的变化。此外，本文还提出了一种新的混合方法，用于预测在用系统的剩余使用寿命，该方法适用于传感器改造后的系统，但缺乏传感器数据。为了生成第一个示例的数据，需要开发和建造合适的滚珠轴承试验台。试验台应允许在速度和轴承载荷方面改变操作条件。运行至故障数据由不同的传感器采集，例如加速度传感器、温度传感器和应变仪。对于第二个例子，还实施了基于压电换能器的实验室实验，其失效的特征在于裂纹，并且应该被监测。第三个例子是基于一个涡轮风扇发动机的模拟数据，其退化在六个条件下已被各种传感器检测到。

Condition-based maintenance and predictive maintenance are increasingly applied in the industry due to their ability of ensuring an optimum utilization of the monitored system. These maintenance strategies allow for diagnosing and predicting the health states of the system under stationary operating conditions. However, technical systems mostly operate under non-stationary conditions, e.g. a wind turbine affected by different loads and speeds due to stochastic wind excitation. Non-stationary conditions lead to changed sensor data and thereby mask alterations caused by either faults or degradation of the system. Therefore, condition monitoring methods need to be extended and adapted for systems operating under non-stationary conditions.The proposed project aims to develop methods for remaining useful lifetime prediction for systems operated under non-stationary conditions. Therefore, classical data-driven and model-based approaches from engineering are combined with approaches from the field of artificial intelligence. By a hybrid combination of clustering and classification with knowledge-based approaches, operating conditions are categorized and failure modes are identified. Based on uncertainty quantification and analyzed relationships between the operating conditions, the sensor data und the degradation evolution, suitable features for enabling the prediction of the remaining useful lifetime are developed and evaluated. Embedding non-stationary future operating conditions is realized by the use of different machine learning methods such as learning on data streams. These methods enable incremental learning and adaption to changes like variation of operating conditions. Moreover, hybrid methods are developed to allow a prediction of the remaining useful lifetime for used systems that are retrofitted with suitable sensors but lack sensor data of their past operation.For validation of the methods for remaining useful lifetime predictions, three application examples are chosen which have been selected from various thematic fields. To generate data for the first example, a suitable ball bearing test rig needs to be developed and constructed. The test rig should allow varying operating conditions regarding speed and bearing load. Run-to-failure data is acquired by different sensors such as acceleration sensors, temperature sensors, and strain gauges. For the second example, a laboratory experiment based on piezoelectric transducers is also implemented, whose failure is characterized by cracks and should be monitored. The third example is based on simulated data of a turbofan engine whose degradation under six conditions has been detected by various sensors.