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能够确保被监控系统的最佳利用而在工业中得到越来越多的应用。这些维护策略允许在固定操作条件下诊断和预测系统的健康状态。然而，技术系统大多在非平稳条件下运行，例如，由于随机风激励，风力涡轮机受到不同负载和速度的影响。非平稳条件导致传感器数据的变化，从而掩盖由故障或系统退化引起的变化。因此，状态监测方法需要扩展和适应在非平稳条件下运行的系统。提出的项目旨在开发在非平稳条件下运行的系统的剩余有用寿命预测方法。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。通过将聚类和分类与基于知识的方法相结合，对运行工况进行分类，识别故障模式。在不确定性量化的基础上，分析了工作条件、传感器数据和退化演变之间的关系，开发并评估了预测剩余使用寿命的合适特征。嵌入非平稳的未来运行条件是通过使用不同的机器学习方法来实现的，例如对数据流的学习。这些方法可以使增量学习和适应变化，如操作条件的变化。此外，开发了混合方法来预测使用过的系统的剩余使用寿命，这些系统经过适当的传感器改造，但缺乏其过去操作的传感器数据。为了验证剩余的有用寿命预测方法，从不同的主题领域中选择了三个应用实例。为了生成第一个示例的数据，需要开发和构建一个合适的滚珠轴承试验台。试验台应允许在速度和轴承负荷方面变化操作条件。运行到故障的数据由不同的传感器获取，如加速度传感器、温度传感器和应变计。对于第二个例子，也进行了基于压电换能器的实验室实验，其失效特征为裂纹，需要进行监测。第三个例子是基于涡扇发动机的模拟数据，该发动机在六种情况下的退化已经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.